Artículos de revistas: "Face editing"

1

Hałaczkiewicz, Joanna. "Elektroniczne wydawanie zbiorów korespondencji – dokonania i perspektywy [Electronic publishing of the collection of letters – achievements and perspectives]." Napis XXIII (2017) (December 24, 2017): 302–13. https://doi.org/10.18318/napis.2017.1.18.

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The article discusses the concept of scholarly edition of electronic correspondence and it also describes its most important features such as interactivity, nonlinearity, processuality. Next, technical aspects of creating electronic editions are examined, particularly the issue of coding in TEI-XML. The example is digital edition of letters and texts by Jan Dantyszek prepared by the team from the Department of Editing Sources and Digital Humanities of Warsaw University. Finally, the text presents the possibilities of developing scholarly e-editing of correspondence in the face of the foretold “death of a printing book”.

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2

Niu, Yongjie, Pengbo Zhou, Hao Chi, and Mingquan Zhou. "Nonlinear hierarchical editing: A powerful framework for face editing." Engineering Applications of Artificial Intelligence 135 (September 2024): 108706. http://dx.doi.org/10.1016/j.engappai.2024.108706.

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Liu, Shuang, Dan Li, Tianchi Cao, Yuke Sun, Yingsong Hu, and Junwen Ji. "GAN-Based Face Attribute Editing." IEEE Access 8 (2020): 34854–67. http://dx.doi.org/10.1109/access.2020.2974043.

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4

Massey, Gregory D. "The Papers of Henry Laurens and Modern Historical Documentary Editing." Public Historian 27, no. 1 (2005): 39–60. http://dx.doi.org/10.1525/tph.2005.27.1.39.

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In early 2003 the Papers of Henry Laurens published the sixteenth and last volume of its letterpress edition. Since its inception over forty years ago, the project has been a microcosm of the changes that have occurred in historical documentary editing. The project pioneered the use of computers to create more accurate and comprehensive indexes. It went further than most projects in adopting a literal transcription policy. Over the past twenty years, the Laurens Papers' difficulties in maintaining a staff and producing volumes in the face of budget constraints mirror the problems faced by other projects as federal support for documentary editing has decreased or remained stagnant.

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5

Niu, Yongjie, Mingquan Zhou, and Zhan Li. "Disentangling the latent space of GANs for semantic face editing." PLOS ONE 18, no. 10 (2023): e0293496. http://dx.doi.org/10.1371/journal.pone.0293496.

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Disentanglement research is a critical and important issue in the field of image editing. In order to perform disentangled editing on images generated by generative models, this paper presents an unsupervised, model-agnostic, two-stage trained editing framework. This work addresses the problem of discovering interpretable, disentangled directions of edited image attributes in the latent space of generative models. This effort’s primary objective was to address the limitations discovered in previous research, mainly (a) the discovered editing directions are interpretable but significantly entangled, i.e., changes to one attribute affect the others and (b) Prior research has utilized direction discovery and direction disentanglement separately, and they can’t work synergistically. More specifically, this paper proposes a two-stage training method that discovers the editing direction with semantics, perturbs the dimension of the direction vector, adjusts it with a penalty mechanism, and makes the editing direction more disentangled. This allows easy distinguishable image editing, such as age and facial expressions in facial images. Experimentally compared to other methods, the proposed method outperforms them both qualitatively and quantitatively in terms of interpretability, disentanglement, and distinguishability of the generated images. The implementation of our method is available at https://github.com/ydniuyongjie/twoStageForFaceEdit.

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6

Song, Linsen, Jie Cao, Lingxiao Song, Yibo Hu, and Ran He. "Geometry-Aware Face Completion and Editing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2506–13. http://dx.doi.org/10.1609/aaai.v33i01.33012506.

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Face completion is a challenging generation task because it requires generating visually pleasing new pixels that are semantically consistent with the unmasked face region. This paper proposes a geometry-aware Face Completion and Editing NETwork (FCENet) by systematically studying facial geometry from the unmasked region. Firstly, a facial geometry estimator is learned to estimate facial landmark heatmaps and parsing maps from the unmasked face image. Then, an encoder-decoder structure generator serves to complete a face image and disentangle its mask areas conditioned on both the masked face image and the estimated facial geometry images. Besides, since low-rank property exists in manually labeled masks, a low-rank regularization term is imposed on the disentangled masks, enforcing our completion network to manage occlusion area with various shape and size. Furthermore, our network can generate diverse results from the same masked input by modifying estimated facial geometry, which provides a flexible mean to edit the completed face appearance. Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well.

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7

Xu, Zhiliang, Xiyu Yu, Zhibin Hong, et al. "FaceController: Controllable Attribute Editing for Face in the Wild." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3083–91. http://dx.doi.org/10.1609/aaai.v35i4.16417.

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Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. By simply employing some existing and easy-obtainable prior information, our method can control, transfer, and edit diverse attributes of faces in the wild. The proposed method can consequently be applied to various applications such as face swapping, face relighting, and makeup transfer. In our method, we decouple identity, expression, pose, and illumination by using 3D priors; separate texture and colors by using region-wise style codes. All the information is embedded into adversarial learning by our identity-style normalization module. Disentanglement losses are proposed to enhance the generator to extract information independently from each attribute. Comprehensive quantitative and qualitative evaluations have been conducted. In a single framework, our method achieves the best or competitive scores on a variety of face applications.

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8

Cao, Zhengcao, Yashu Wang, Liangyu Wu, et al. "Reexamining the Kuleshov effect: Behavioral and neural evidence from authentic film experiments." PLOS ONE 19, no. 8 (2024): e0308295. http://dx.doi.org/10.1371/journal.pone.0308295.

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Film cognition explores the influence of cinematic elements, such as editing and film color, on viewers’ perception. The Kuleshov effect, a famous example of how editing influences viewers’ emotional perception, was initially proposed to support montage theory through the Kuleshov experiment. This effect, which has since been recognized as a manifestation of point-of-view (POV) editing practices, posits that the emotional interpretation of neutral facial expressions is influenced by the accompanying emotional scene in a face-scene-face sequence. However, concerns persist regarding the validity of previous studies, often employing inauthentic film materials like static images, leaving the question of its existence in authentic films unanswered. This study addresses these concerns by utilizing authentic films in two experiments. In Experiment 1, multiple film clips were captured under the guidance of a professional film director and seamlessly integrated into authentic film sequences. 59 participants viewed these face-scene-face film sequences and were tasked with rating the valence and emotional intensity of neutral faces. The findings revealed that the accompanying fearful or happy scenes significantly influence the interpretation of emotion on neutral faces, eliciting perceptions of negative or positive emotions from the neutral face. These results affirm the existence of the Kuleshov effect within authentic films. In Experiment 2, 31 participants rated the valence and arousal of neutral faces while undergoing functional magnetic resonance imaging (fMRI). The behavioral results confirm the Kuleshov effect in the MRI scanner, while the neural data identify neural correlates that support its existence at the neural level. These correlates include the cuneus, precuneus, hippocampus, parahippocampal gyrus, post cingulate gyrus, orbitofrontal cortex, fusiform gyrus, and insula. These findings also underscore the contextual framing inherent in the Kuleshov effect. Overall, the study integrates film theory and cognitive neuroscience experiments, providing robust evidence supporting the existence of the Kuleshov effect through both subjective ratings and objective neuroimaging measurements. This research also contributes to a deeper understanding of the impact of film editing on viewers’ emotional perception from the contemporary POV editing practices and neurocinematic perspective, advancing the knowledge of film cognition.

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9

Ambroziak, Tomasz. "Jak wydawać cyrylickie akta sejmikowe? Analiza rosyjskich, ukraińskich i białoruskich współczesnych zasad wydawniczych oraz wybranej praktyki edytorskiej. Część I." Miscellanea Historico-Iuridica 21, no. 1 (2022): 321–45. http://dx.doi.org/10.15290/mhi.2022.21.01.11.

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One area in which significant progress has been made in the publication of sources in recent years is the editing of sejmik records. The fundamental question facing publishers of sources is the issue of editing priniples. Publishers of Lithuanian sejmik records are in a peculiar situation, as they face the problem of publishing sources in Polish and Ruthenian in one volume. Meanwhile, in Polish editing practice, there are no strictly defined rules for publishing Cyrillic sources, and contemporary experience in this matter is quite modest. The team preparing the edition of the sejmik records of the Vilnius voivodship from 1566–1655 faced a similar problem. In the course of the work, in order to determine the principles of editing the text, the existing theoretical models and solutions adopted in publishing practice were analyzed and evaluated for their suitability for the tasks ahead. This article will analyze contemporary publishing instructions and methodological recommendations for the publication of Cyrillic sources formulated in Russian, Belarusian and Ukrainian editing, as well as selected source publications of Cyrillic documents from the territory of the Grand Duchy of Lithuania from the 16th–17th centuries. The conclusions of the analysis will be used to formulate proposals for specific solutions for the publishers of sejmik records.

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10

Xiao, Zhujun, Jenna Cryan, Yuanshun Yao, et al. ""My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing." Proceedings on Privacy Enhancing Technologies 2023, no. 3 (2023): 252–67. http://dx.doi.org/10.56553/popets-2023-0080.

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Today, face editing is widely used to refine/alter photos in both professional and recreational settings. Yet it is also used to modify (and repost) existing online photos for cyberbullying. Our work considers an important open question: 'How can we support the collaborative use of face editing on social platforms while protecting against unacceptable edits and reposts by others?' This is challenging because, as our user study shows, users vary widely in their definition of what edits are (un)acceptable. Any global filter policy deployed by social platforms is unlikely to address the needs of all users, but hinders social interactions enabled by photo editing. Instead, we argue that face edit protection policies should be implemented by social platforms based on individual user preferences. When posting an original photo online, a user can choose to specify the types of face edits (dis)allowed on the photo. Social platforms use these per-photo edit policies to moderate future photo uploads, i.e., edited photos containing modifications that violate the original photo's policy are either blocked or shelved for user approval. Realizing this personalized protection, however, faces two immediate challenges: (1) how to accurately recognize specific modifications, if any, contained in a photo; and (2) how to associate an edited photo with its original photo (and thus the edit policy). We show that these challenges can be addressed by combining highly efficient hashing based image search and scalable semantic image comparison, and build a prototype protector (Alethia) covering nine edit types. Evaluations using IRB-approved user studies and data-driven experiments (on 839K face photos) show that Alethia accurately recognizes edited photos that violate user policies and induces a feeling of protection to study participants. This demonstrates the initial feasibility of personalized face edit protection. We also discuss current limitations and future directions to push the concept forward.

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11

Upadhyay, Mukunda, Badri Raj Lamichhane, and Bal Krishna Nyaupane. "Facial Attribute Editing Using Generative Adversarial Network." Journal of Engineering and Sciences 2, no. 1 (2023): 57–63. http://dx.doi.org/10.3126/jes2.v2i1.60394.

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Facial attribute editing tasks have immense applications in today’s digital world, including virtual makeup, generating faces in the animation and gaming industry, social media face image enhancement and improving face recognition systems. This task can be achieved manually or automatically. Manual facial attribute editing, performed with software such as Adobe Photoshop, is a tedious and time-consuming process that requires an expert person. However, Automatic facial attribute editing tasks that can perform facial attribute editing within a few seconds are achievable using encoder-decoder and deep learning-based generative models, such as conditional Generative Adversarial Networks. In our work, we use different attribute vectors as conditional information to generate desired target images, and encoder-decoder structures incorporate feature transfer units to choose and alter encoder-based features. Later, these encoder features are concatenated with the decoder feature to strengthen the attribute editing ability of the model. For this research, we apply reconstruction loss to preserve other details of a face image except target attributes. Adversarial loss is employed for visually realistic editing and attribute manipulation loss is employed to ensure that the generated image possesses the correct attributes. Furthermore, we adopt the WGAN-GP loss function type to improve training stability and reduce the mode collapse problem that often occurs in GAN. Experiments on the Celebi dataset show that this method produces visually realistic facial attribute edited images with PSNR/SSIM 31.7/0.95 and 89.23 % of average attribute editing accuracy for 13 facial attributes including Bangs, Mustache, Bald, Bushy Eyebrows, Blond Hair, Eyeglasses, Black Hair, Brown Hair, Mouth Slightly Open, Male, No Beard, pale Skin and Young.

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12

Oktaviani, Reni, Siti Ansoriyah, and Etsa Purbarani. "Syllabus Development of Language Editing Courses Indonesia Based on Information and Communication Technology Integrated XXI Century." Aksis : Jurnal Pendidikan Bahasa dan Sastra Indonesia 6, no. 1 (2022): 52–61. http://dx.doi.org/10.21009/aksis.060105.

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The background of this research is about a pandemic that has changed learning patterns from face-to-face to face-to-face. This causes the syllabus that existed before the pandemic to adapt to current conditions. In addition, this syllabus applies a project-based learning model and case studies. The purpose of this research is to explain and develop the syllabus of the Indonesian Language Editing course based on integrated Information and Communication Technology (ICT) in the XXI century. This research uses research and development. The stages in this research, namely: first, explain the process of developing the syllabus for the integrated ICT-based Indonesian language editing course in the XXI century. Second, developing a syllabus for the XXI century integrated ICT-based Indonesian Language Editing course. Data collection techniques were obtained through tests and non-tests in the form of observations, questionnaires, and documentation and then processed using content analysis techniques. Based on this, the development of an integrated ICT-based syllabus in the XXI century was applied to level IV students of the Indonesian Language and Literature Education Study Program to obtain effective learning outcomes in the Indonesian Language Editing course.

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13

Jagadish, P., B. Nethravathi, Sandhyarani, C. Chirag, Bhagyashre, and C. S. Gokhul. "FaceForge: Personalized Facial Enhancement with Class-guided Noise Reduction." Journal of Research and Review: Machine Learning 1, no. 2 (2025): 20–28. https://doi.org/10.5281/zenodo.14874554.

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Personalizing digital faces is a fundamental task in various fields such as virtual try-on, digital avatars, and face image editing. However, achieving realistic and editable face personalization remains a challenge due to the inherent noise and variability of face images. This study presents FaceForge, a new approach to editable face personalization using class-driven noise suppression regulation. FaceForge uses a diffusion-based generative model trained with class-specific information to enable high-quality facial manipulation while preserving identity and expression details. The proposed method effectively suppresses noise and artifacts in face images, allowing users to personalize faces with unprecedented fidelity and control. Experimental results demonstrate the superior performance of FaceForge compared to state-of-the-art methods in terms of image quality, editability, and robustness to input variations. In addition, we demonstrate the practical implications of FaceForge in real-world applications such as virtual testing systems and the creation of digital avatars. Overall, FaceForge represents a significant advance in the field of facial personalization, offering users a  powerful tool to easily create personalized and distinctive digital faces.

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14

Pang, Min, Ligang He, Liqun Kuang, Min Chang, Zhiying He, and Xie Han. "Developing a Parametric 3D Face Model Editing Algorithm." IEEE Access 8 (2020): 167209–24. http://dx.doi.org/10.1109/access.2020.3022987.

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Li, Xiaoyan, Tongliang Liu, Jiankang Deng, and Dacheng Tao. "Video Face Editing Using Temporal-Spatial-Smooth Warping." ACM Transactions on Intelligent Systems and Technology 7, no. 3 (2016): 1–28. http://dx.doi.org/10.1145/2819000.

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16

Song, Xiaoxia, Mingwen Shao, Wangmeng Zuo, and Cunhe Li. "Face attribute editing based on generative adversarial networks." Signal, Image and Video Processing 14, no. 6 (2020): 1217–25. http://dx.doi.org/10.1007/s11760-020-01660-0.

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17

Hair, P. E. H. "On Editing Barbot." History in Africa 20 (1993): 53–59. http://dx.doi.org/10.2307/3171964.

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In 1974 the very first issue of HA included an analysis of a small section of John Barbot's Description of the coasts of North and South Guinea. Since this represented the first fruits of a project to edit Barbot's writings on Guinea, it is appropriate that, now the completed edition is published, a review of the history of the editing, the methods and problems of the editors, and the problems that the consumer will face in using the edition, should also appear in HA.Why Barbot? When, twenty years ago, I decided that Barbot's account of Guinea should be edited, I already knew that it was partly unoriginal, and that in an ideal world priority would be given to editing the other, earlier, recognized compendium on Guinea, the relevant section of Dapper's account of all Africa. For although Dapper is also partly unoriginal, it has probably a wider range of new material than Barbot, not least the very detailed Kquoja account. Why then Barbot rather than Dapper? The answer is simple. I recognized the lack of critical editing of Guinea sources and felt I had to take the plunge somewhere. But whereas Dapper wrote in Dutch, a language of which I have only dictionary command, the earlier manuscript version of Barbot was in French, a language I could cope with. Dapper will have his turn. Adam Jones, one of the co-editors of “Barbot on Guinea,” having Dutch, has already published studies of Dapper's sources. Moreover, in the edition of Barbot we have taken the unusual step of including in the annotation fairly frequent references to the lines of transmission of information, for instance, not only noting the material Barbot borrowed from Dapper but also, where the material was not original to Dapper, the sources of his borrowing—thus doing part of the work of a critical edition of Dapper. In fact we have generally tried to make the edition of Barbot a starting point for the critical study of many other pre-1700 Guinea sources.

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Hays, Richard, Trevor Gibbs, Julie Hunt, Barbara Jennings, and Ken Masters. "The changing face of MedEdPublish." MedEdPublish 11 (November 2, 2021): 1. http://dx.doi.org/10.12688/mep.17500.1.

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MedEdPublish has come a long way since it was launched in 2016 by AMEE as an independent academic e-journal that supports scholarship in health professions education. Beginning as a relatively small, in-house publication on a web platform adapted for the purpose, we invited members of our community of practice to submit articles on any topic in health professions education, and encouraged a wide range of article types. All articles were published so long as they met editing criteria and where within scope. Reviews were welcomed from both members of our Review panel and the general readership, all published openly with contributors identified. Many articles attracted several reviews, responses and comments, creating interactive discussion threads that provided learning opportunities for all. The outcome surpassed our expectations, with over 500 articles submitted during 2020, beyond the capacity of our editing team and platform to achieve our promise of rapid publishing. We have now moved to a much larger and powerful web platform, developed by F1000 Research and within the Taylor and Francis stable, the home of AMEE’s other journal, Medical Teacher. Most of our innovations are supported by the new platform and there is scope for further developments. We look forward to an exciting new phase of innovation, powered by the F1000 platform.

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19

Pana Pramulia, Mimas Ardhianti, and Sri Budi Astuti. "Pelatihan Penyuntingan Karya Tulis Ilmiah Populer Untuk Siswa Kelas XII SMA Negeri 1 Kedamean." Sejahtera: Jurnal Inspirasi Mengabdi Untuk Negeri 3, no. 1 (2023): 157–64. http://dx.doi.org/10.58192/sejahtera.v3i1.1712.

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One form of writing, namely popular scientific works. In writing popular scientific works, writers must follow predetermined guidelines, for example templates, word choice (diction), and suitability of themes. Popular scientific works have word choices that are easy to read and easy for various groups to understand. Therefore, an editing process is needed so that popular scientific works become good. The aim of editing is to avoid too many errors and add references if necessary. Editing is the author's final step before his writing is published. Editing is not an easy job. This was experienced by the students of SMA Negeri 1 Kedamean. For this reason, lecturers in the Indonesian Language Education Study Program at PGRI Adi Buana University Surabaya need to provide community service through training. Training implementation method with face-to-face presentations, modeling and simulation accompanied by assignments. The community service carried out can provide students with insight and skills in editing popular scientific papers.

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20

Park, Jungsik, Byung-Kuk Seo, and Jong-Il Park. "A Framework for Real-Time 3D Freeform Manipulation of Facial Video." Applied Sciences 9, no. 21 (2019): 4707. http://dx.doi.org/10.3390/app9214707.

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This paper proposes a framework that allows 3D freeform manipulation of a face in live video. Unlike existing approaches, the proposed framework provides natural 3D manipulation of a face without background distortion and interactive face editing by a user’s input, which leads to freeform manipulation without any limitation of range or shape. To achieve these features, a 3D morphable face model is fitted to a face region in a video frame and is deformed by the user’s input. The video frame is then mapped as a texture to the deformed model, and the model is rendered on the video frame. Because of the high computational cost, parallelization and acceleration schemes are also adopted for real-time performance. Performance evaluation and comparison results show that the proposed framework is promising for 3D face editing in live video.

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21

Sun, Jingxiang, Xuan Wang, Yichun Shi, Lizhen Wang, Jue Wang, and Yebin Liu. "IDE-3D." ACM Transactions on Graphics 41, no. 6 (2022): 1–10. http://dx.doi.org/10.1145/3550454.3555506.

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Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution, or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initializes the latent codes from the semantic and texture encoder, and further optimizes them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and produces high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness and efficiency.

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Lumbantoruan, Gortap, Marlyna Infryanty Hutapea, Jamaluddin Jamaluddin, et al. "PELATIHAN VIDEO RECORDING DAN EDITING VIDEO PADA SMK SWASTA GELORA JAYA NUSANTARA MEDAN." Jurnal Pengabdian Pada Masyarakat METHABDI 1, no. 1 (2021): 1–4. http://dx.doi.org/10.46880/methabdi.vol1no1.pp1-4.

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The purpose of community service activities is to implement the “Tri Dharma” of Higher Education as well as to contribute ideas and transfer technology to teaching staff at SMK Gelora Jaya Nusantara Medan. This service activity was carried out for 2 days, with Video Recording and Video Editing Training materials. The topics given in this training are making video editing media and online learning content. The material given is the use of Filmora X software in video editing, and video recording techniques. This topic is very much needed in order to equip teachers in preparing and delivering subject matter during this COVID-19 pandemic. This topic was deliberately chosen considering that currently teachers are having difficulties in delivering subject matter face-to-face.

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Gao, Tianle, Yaojun Li, and Yiwen Zhao. "CRISPR/Cas base-editing systems and their potential applications and prospects." Theoretical and Natural Science 20, no. 1 (2023): 135–40. http://dx.doi.org/10.54254/2753-8818/20/20230739.

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CRISPR-Cas9 and its derivatives such as cytosine base editor, adenine base editor and prime editing are an important topic of research today. In recent years, with continuous development and updates by researchers, editing systems have been able to achieve various modifications of target genes. For example, base substitution, insertion and deletion of short fragment genes, etc. have wide applications in plants and animals, etc. However, these editing systems derived from the CRISPR/Cas9 system still face challenges in terms of editing efficiency and accuracy. This review describes the current status of research using editing systems in plant breeding, gene disease treatment, and knockdown of specific genes by establishing animal models, analyzes the advantages and disadvantages of editing technologies, and looks forward to their development.

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Zhang, Yu, and Edmond C. Prakash. "Face to Face: Anthropometry-Based Interactive Face Shape Modeling Using Model Priors." International Journal of Computer Games Technology 2009 (2009): 1–15. http://dx.doi.org/10.1155/2009/573924.

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This paper presents a new anthropometrics-based method for generating realistic, controllable face models. Our method establishes an intuitive and efficient interface to facilitate procedures for interactive 3D face modeling and editing. It takes 3D face scans as examples in order to exploit the variations presented in the real faces of individuals. The system automatically learns a model prior from the data-sets of example meshes of facial features using principal component analysis (PCA) and uses it to regulate the naturalness of synthesized faces. For each facial feature, we compute a set of anthropometric measurements to parameterize the example meshes into a measurement space. Using PCA coefficients as a compact shape representation, we formulate the face modeling problem in a scattered data interpolation framework which takes the user-specified anthropometric parameters as input. Solving the interpolation problem in a reduced subspace allows us to generate a natural face shape that satisfies the user-specified constraints. At runtime, the new face shape can be generated at an interactive rate. We demonstrate the utility of our method by presenting several applications, including analysis of facial features of subjects in different race groups, facial feature transfer, and adapting face models to a particular population group.

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25

Ju, Yixuan, Jianhai Zhang, Xiaoyang Mao, and Jiayi Xu. "Adaptive semantic attribute decoupling for precise face image editing." Visual Computer 37, no. 9-11 (2021): 2907–18. http://dx.doi.org/10.1007/s00371-021-02198-z.

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26

Liu, Kanglin, Gaofeng Cao, Fei Zhou, Bozhi Liu, Jiang Duan, and Guoping Qiu. "Towards Disentangling Latent Space for Unsupervised Semantic Face Editing." IEEE Transactions on Image Processing 31 (2022): 1475–89. http://dx.doi.org/10.1109/tip.2022.3142527.

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27

Gwin, Louis. "Prospective Reporters Face Writing/Editing Tests at Many Dailies." Newspaper Research Journal 9, no. 2 (1988): 101–11. http://dx.doi.org/10.1177/073953298800900210.

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This national study confirms a documented trend that daily newspapers are using writing and editing tests to evaluate prospective reporters. It finds that nearly 45% of responding newspapers administer such tests and that the likelihood of testing decreases as circulation size increases. Those managing editors who do test, consider test results a key factor in reaching a hiring decision on both experienced and inexperienced candidates for reporting jobs.

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28

Deng, Qiyao, Qi Li, Jie Cao, Yunfan Liu, and Zhenan Sun. "Controllable Multi-Attribute Editing of High-Resolution Face Images." IEEE Transactions on Information Forensics and Security 16 (2021): 1410–23. http://dx.doi.org/10.1109/tifs.2020.3033184.

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29

Guo, Ming, Feng Xu, Shunfei Wang, et al. "Synthesis, Style Editing, and Animation of 3D Cartoon Face." Tsinghua Science and Technology 29, no. 2 (2024): 506–16. http://dx.doi.org/10.26599/tst.2023.9010028.

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30

Baranowski, Andreas M., and H. Hecht. "The Auditory Kuleshov Effect: Multisensory Integration in Movie Editing." Perception 46, no. 5 (2016): 624–31. http://dx.doi.org/10.1177/0301006616682754.

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Almost a hundred years ago, the Russian filmmaker Lev Kuleshov conducted his now famous editing experiment in which different objects were added to a given film scene featuring a neutral face. It is said that the audience interpreted the unchanged facial expression as a function of the added object (e.g., an added soup made the face express hunger). This interaction effect has been dubbed “Kuleshov effect.” In the current study, we explored the role of sound in the evaluation of facial expressions in films. Thirty participants watched different clips of faces that were intercut with neutral scenes, featuring either happy music, sad music, or no music at all. This was crossed with the facial expressions of happy, sad, or neutral. We found that the music significantly influenced participants’ emotional judgments of facial expression. Thus, the intersensory effects of music are more specific than previously thought. They alter the evaluation of film scenes and can give meaning to ambiguous situations.

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31

Wang, Lyu, and Xiangling Wang. "Building virtual communities of practice in post-editing training: A mixed-method quasi-experimental study." Journal of Specialised Translation, no. 36 (July 25, 2021): 193–219. https://doi.org/10.26034/cm.jostrans.2021.041.

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Since post-editing of machine translation has become a widespread practice in the language service industry, post-editing training has been increasingly included in translation courses. The purpose of the study is to investigate the feasibility of implementing a virtual community of practice (VCoP) in training future post-editors that meet the market's needs. A VCoP refers to a social group sharing a common interest in an argument or a problem, interacting with each other by networks (Davis and Goodman 2014). Based on this concept, a VCoP method is postulated to facilitate online post-editing courses. It is a method in which students learn how to post-edit and how to manage a post-editing project by actively engaging in a VCoP. To test the feasibility of this method, a quasi-experimental study was carried out at Hunan University in China. Thirty first-year translation postgraduates participated in the study. Fifteen students were taught virtually using the VCoP method, while fifteen students were taught by the same tutor in a face-to-face classroom. A comparison of students' performance in the two post-editing classes indicated that the VCoP method fostered students' self-reflection and self-assessment, and ultimately helped improve post-editing quality. Students' perceptions of the VCoP method were generally positive, showing its usability.

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32

Juengst, Eric, Michael A. Flatt, John M. Conley, et al. "Preventive Human Genome Editing and Enhancement: Candidate Criteria for Governance." Hastings Center Report 54, no. 5 (2024): 14–23. http://dx.doi.org/10.1002/hast.4913.

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AbstractWhile somatic cell editing to treat disease is widely accepted, the use of human genome editing for “enhancement” remains contested. Scientists and policy‐makers routinely cite the prospect of enhancement as a salient ethical challenge for human genome editing research. If preventive genome editing projects are perceived as pursuing human enhancement, they could face heightened barriers to scientific, public, and regulatory approval. This article outlines what we call “preventive strengthening research” (or “PSR”) to explore, through this example, how working to strengthen individuals’ resistance to disease beyond what biomedicine considers to be the human functional range may be interpreted as pursuing human enhancement. Those involved in developing guidance for PSR will need to navigate the interface between preventive goals and enhancement implications. This article identifies and critiques three of these ideas in the interest of anticipating the wider emergence of PSR and the need for a normative approach for its pursuit. All three “candidate criteria” merit attention, but each also faces challenges that will need to be addressed as further research policy is developed.

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33

Zhang, Qian, Vikas Thamizharasan, and James Tompkin. "Learning physically based material and lighting decompositions for face editing." Computational Visual Media 10, no. 2 (2024): 295–308. http://dx.doi.org/10.1007/s41095-022-0309-1.

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AbstractLighting is crucial for portrait photography, yet the complex interactions between the skin and incident light are expensive to model computationally in graphics and difficult to reconstruct analytically via computer vision. Alternatively, to allow fast and controllable reflectance and lighting editing, we developed a physically based decomposition through deep learned priors from path-traced portrait images. Previous approaches that used simplified material models or low-frequency or low-dynamic-range lighting struggled to model specular reflections or relight directly without intermediate decomposition. However, we estimate the surface normal, skin albedo and roughness, and high-frequency HDRI maps, and propose an architecture to estimate both diffuse and specular reflectance components. In our experiments, we show that this approach can represent the true appearance function more effectively than simpler baseline methods, leading to better generalization and higher-quality editing.

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34

Leimkühler, Thomas, and George Drettakis. "FreeStyleGAN." ACM Transactions on Graphics 40, no. 6 (2021): 1–15. http://dx.doi.org/10.1145/3478513.3480538.

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Current Generative Adversarial Networks (GANs) produce photorealistic renderings of portrait images. Embedding real images into the latent space of such models enables high-level image editing. While recent methods provide considerable semantic control over the (re-)generated images, they can only generate a limited set of viewpoints and cannot explicitly control the camera. Such 3D camera control is required for 3D virtual and mixed reality applications. In our solution, we use a few images of a face to perform 3D reconstruction, and we introduce the notion of the GAN camera manifold, the key element allowing us to precisely define the range of images that the GAN can reproduce in a stable manner. We train a small face-specific neural implicit representation network to map a captured face to this manifold and complement it with a warping scheme to obtain free-viewpoint novel-view synthesis. We show how our approach - due to its precise camera control - enables the integration of a pre-trained StyleGAN into standard 3D rendering pipelines, allowing e.g., stereo rendering or consistent insertion of faces in synthetic 3D environments. Our solution proposes the first truly free-viewpoint rendering of realistic faces at interactive rates, using only a small number of casual photos as input, while simultaneously allowing semantic editing capabilities, such as facial expression or lighting changes.

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35

Shapiro, Maura. "CRISPR-Cas9 examined for potential and challenges treating Duchenne Muscular Dystrophy." Scilight 2023, no. 8 (2023): 081103. http://dx.doi.org/10.1063/10.0017423.

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36

Yunes, Maria Cristina, Zimbábwe Osório-Santos, Marina A. G. von Keyserlingk, and Maria José Hötzel. "Gene Editing for Improved Animal Welfare and Production Traits in Cattle: Will This Technology Be Embraced or Rejected by the Public?" Sustainability 13, no. 9 (2021): 4966. http://dx.doi.org/10.3390/su13094966.

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Integrating technology into agricultural systems has gained considerable traction, particularly over the last half century. Agricultural systems that incorporate the public’s concerns regarding farm animal welfare are more likely to be socially accepted in the long term, a key but often forgotten component of sustainability. Gene editing is a tool that has received considerable attention in the last five years, given its potential capacity to improve farm animal health, welfare, and production efficiency. This study aimed to explore the attitudes of Brazilian citizens regarding the applications of gene editing in cattle that generate offspring without horns; are more resistant to heat; and have increased muscle tissue. Using a mixed-methods approach, we surveyed participants via face-to-face, using in-depth interviews (Study 1) and an online questionnaire containing closed-ended questions (Study 2). Overall, the acceptability of gene editing was low and in cases where support was given it was highly dependent on the type and purpose of the application proposed. Using gene editing to improve muscle tissue growth was viewed as less acceptable compared to using gene editing to reduce heat stress or to produce hornless cattle. Support declined when the application was perceived to harm animal welfare, to be profit motivated or to reinforce the status quo of intensive livestock systems. The acceptability of gene editing was reduced when perceptions of risks and benefits were viewed as unevenly or unfairly distributed among consumers, corporations, different types of farmers, and the animals. Interviewees did not consider gene editing a “natural” process, citing dissenting reasons such as the high degree of human interference and the acceleration of natural processes. Our findings raised several issues that may need to be addressed for gene editing to comply with the social pillar of sustainable agriculture.

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37

Kawabe, Akihisa, Ryuto Haga, Yoichi Tomioka, Jungpil Shin, and Yuichi Okuyama. "A Dynamic Ensemble Selection of Deepfake Detectors Specialized for Individual Face Parts." Electronics 12, no. 18 (2023): 3932. http://dx.doi.org/10.3390/electronics12183932.

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The development of deepfake technology, based on deep learning, has made it easier to create images of fake human faces that are indistinguishable from the real thing. Many deepfake methods and programs are publicly available and can be used maliciously, for example, by creating fake social media accounts with images of non-existent human faces. To prevent the misuse of such fake images, several deepfake detection methods have been proposed as a countermeasure and have proven capable of detecting deepfakes with high accuracy when the target deepfake model has been identified. However, the existing approaches are not robust to partial editing and/or occlusion caused by masks, glasses, or manual editing, all of which can lead to an unacceptable drop in accuracy. In this paper, we propose a novel deepfake detection approach based on a dynamic configuration of an ensemble model that consists of deepfake detectors. These deepfake detectors are based on convolutional neural networks (CNNs) and are specialized to detect deepfakes by focusing on individual parts of the face. We demonstrate that a dynamic selection of face parts and an ensemble of selected CNN models is effective at realizing highly accurate deepfake detection even from partly edited and occluded images.

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38

Liu, Feng-Lin, Shu-Yu Chen, Yu-Kun Lai, et al. "DeepFaceVideoEditing." ACM Transactions on Graphics 41, no. 4 (2022): 1–16. http://dx.doi.org/10.1145/3528223.3530056.

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Sketches, which are simple and concise, have been used in recent deep image synthesis methods to allow intuitive generation and editing of facial images. However, it is nontrivial to extend such methods to video editing due to various challenges, ranging from appropriate manipulation propagation and fusion of multiple editing operations to ensure temporal coherence and visual quality. To address these issues, we propose a novel sketch-based facial video editing framework, in which we represent editing manipulations in latent space and propose specific propagation and fusion modules to generate high-quality video editing results based on StyleGAN3. Specifically, we first design an optimization approach to represent sketch editing manipulations by editing vectors, which are propagated to the whole video sequence using a proper strategy to cope with different editing needs. Specifically, input editing operations are classified into two categories: temporally consistent editing and temporally variant editing. The former (e.g., change of face shape) is applied to the whole video sequence directly, while the latter (e.g., change of facial expression or dynamics) is propagated with the guidance of expression or only affects adjacent frames in a given time window. Since users often perform different editing operations in multiple frames, we further present a region-aware fusion approach to fuse diverse editing effects. Our method supports video editing on facial structure and expression movement by sketch, which cannot be achieved by previous works. Both qualitative and quantitative evaluations show the superior editing ability of our system to existing and alternative solutions.

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39

Takemae, Yoshinao. "Experimental Evaluation of Video Editing Based on Participants' Gaze for Face-to-face Multiparty Conversations." Journal of the Institute of Image Information and Television Engineers 59, no. 12 (2005): 1822–29. http://dx.doi.org/10.3169/itej.59.1822.

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40

Lin, Yu-Tzu, Cheng-Chih Wu, and Chiung-Fang Chiu. "The Use of Wiki in Teaching Programming." International Journal of Distance Education Technologies 16, no. 3 (2018): 18–45. http://dx.doi.org/10.4018/ijdet.2018070102.

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This article explores the feasibility of employing cooperative program editing tools in teaching programming. A quasi-experimental study was conducted, in which the experimental group co-edited the programs with peers using the wiki. The control group co-edited the programs with peers using only the face-to-face approach. The findings show that the co-editing platform was effective in assisting collaborative learning of programming, especially for program implementation. By observing editing histories, students could compare programs and then reflect more deeply about programming. The use of the wiki history tool also helped to illuminate nonlinear and dynamic procedures utilized in programming. Students who engaged more in the collaborative programming or interacted more with partners on the wiki showed greater program implementation achievements. The major benefit of using the wiki was the enhanced ability to observe the dynamic programming procedure and to encounter programming conflicts, which contributed to the process of procedural knowledge acquisition and elaboration.

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41

Xu, Sen-Zhe, Hao-Zhi Huang, Fang-Lue Zhang, and Song-Hai Zhang. "FaceShapeGene: A disentangled shape representation for flexible face image editing." Graphics and Visual Computing 4 (June 2021): 200023. http://dx.doi.org/10.1016/j.gvc.2021.200023.

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42

Hou, Xianxu, Xiaokang Zhang, Hanbang Liang, Linlin Shen, Zhihui Lai, and Jun Wan. "GuidedStyle: Attribute knowledge guided style manipulation for semantic face editing." Neural Networks 145 (January 2022): 209–20. http://dx.doi.org/10.1016/j.neunet.2021.10.017.

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43

Mao, Aihua, and Hengge Situ. "Image-Driven Automatic 3D Human Face Modeling and Editing Algorithm." Journal of Computer-Aided Design & Computer Graphics 31, no. 1 (2019): 17. http://dx.doi.org/10.3724/sp.j.1089.2019.17362.

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44

Ning, Xin, Shaohui Xu, Weijun Li, and Shuai Nie. "FEGAN: Flexible and Efficient Face Editing With Pre-Trained Generator." IEEE Access 8 (2020): 65340–50. http://dx.doi.org/10.1109/access.2020.2985086.

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45

Zhang, Yu, and Norman I. Badler. "Face modeling and editing with statistical local feature control models." International Journal of Imaging Systems and Technology 17, no. 6 (2007): 341–58. http://dx.doi.org/10.1002/ima.20127.

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46

Aliari, Mohammad Amin, Andre Beauchamp, Tiberiu Popa, and Eric Paquette. "Face Editing Using Part‐Based Optimization of the Latent Space." Computer Graphics Forum 42, no. 2 (2023): 269–79. http://dx.doi.org/10.1111/cgf.14760.

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47

Fontanini, Tomaso, Claudio Ferrari, Giuseppe Lisanti, et al. "FrankenMask: Manipulating semantic masks with transformers for face parts editing." Pattern Recognition Letters 176 (December 2023): 14–20. http://dx.doi.org/10.1016/j.patrec.2023.10.010.

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48

BJ, Sowmya, Meeradevi Meeradevi, and Seems Shedole. "Generative adversarial networks with attentional multimodal for human face synthesis." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1205–15. https://doi.org/10.11591/ijeecs.v33.i2.pp1205-1215.

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Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.

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49

Zegeye, Workie Anley, Mesfin Tsegaw, Yingxin Zhang, and Liyong Cao. "CRISPR-Based Genome Editing: Advancements and Opportunities for Rice Improvement." International Journal of Molecular Sciences 23, no. 8 (2022): 4454. http://dx.doi.org/10.3390/ijms23084454.

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To increase the potentiality of crop production for future food security, new technologies for plant breeding are required, including genome editing technology—being one of the most promising. Genome editing with the CRISPR/Cas system has attracted researchers in the last decade as a safer and easier tool for genome editing in a variety of living organisms including rice. Genome editing has transformed agriculture by reducing biotic and abiotic stresses and increasing yield. Recently, genome editing technologies have been developed quickly in order to avoid the challenges that genetically modified crops face. Developing transgenic-free edited plants without introducing foreign DNA has received regulatory approval in a number of countries. Several ongoing efforts from various countries are rapidly expanding to adopt the innovations. This review covers the mechanisms of CRISPR/Cas9, comparisons of CRISPR/Cas9 with other gene-editing technologies—including newly emerged Cas variants—and focuses on CRISPR/Cas9-targeted genes for rice crop improvement. We have further highlighted CRISPR/Cas9 vector construction model design and different bioinformatics tools for target site selection.

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50

BJ, Sowmya, Meeradevi Meeradevi, and Seems Shedole. "Generative adversarial networks with attentional multimodal for human face synthesis." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1205. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp1205-1215.

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Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.

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