Готові списки джерел за темами / Pose-aware

Добірка наукової літератури з теми "Pose-aware"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Pose-aware"

Wu, Lele, Zhenbo Yu, Yijiang Liu, and Qingshan Liu. "Limb Pose Aware Networks for Monocular 3D Pose Estimation." IEEE Transactions on Image Processing 31 (2022): 906–17. http://dx.doi.org/10.1109/tip.2021.3136613.

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Teller, S., Jiawen Chen, and H. Balakrishnan. "Pervasive pose-aware applications and infrastructure." IEEE Computer Graphics and Applications 23, no. 4 (July 2003): 14–18. http://dx.doi.org/10.1109/mcg.2003.1210859.

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Masi, Iacopo, Feng-Ju Chang, Jongmoo Choi, Shai Harel, Jungyeon Kim, KangGeon Kim, Jatuporn Leksut, et al. "Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 2 (February 1, 2019): 379–93. http://dx.doi.org/10.1109/tpami.2018.2792452.

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Xiao, Yabo, Dongdong Yu, Xiao Juan Wang, Lei Jin, Guoli Wang, and Qian Zhang. "Learning Quality-Aware Representation for Multi-Person Pose Regression." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2822–30. http://dx.doi.org/10.1609/aaai.v36i3.20186.

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Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i.e., confidence of the instance localization) to indicate the pose quality for selecting the pose candidates. We consider that there are two gaps involved in existing paradigm: 1) The instance score is not well interrelated with the pose regression quality. 2) The instance feature representation, which is used for predicting the instance score, does not explicitly encode the structural pose information to predict the reasonable score that represents pose regression quality. To address the aforementioned issues, we propose to learn the pose regression quality-aware representation. Concretely, for the first gap, instead of using the previous instance confidence label (e.g., discrete {1,0} or Gaussian representation) to denote the position and confidence for person instance, we firstly introduce the Consistent Instance Representation (CIR) that unifies the pose regression quality score of instance and the confidence of background into a pixel-wise score map to calibrates the inconsistency between instance score and pose regression quality. To fill the second gap, we further present the Query Encoding Module (QEM) including the Keypoint Query Encoding (KQE) to encode the positional and semantic information for each keypoint and the Pose Query Encoding (PQE) which explicitly encodes the predicted structural pose information to better fit the Consistent Instance Representation (CIR). By using the proposed components, we significantly alleviate the above gaps. Our method outperforms previous single-stage regression-based even bottom-up methods and achieves the state-of-the-art result of 71.7 AP on MS COCO test-dev set.

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Yu, Han, Congju Du, and Li Yu. "Scale-aware heatmap representation for human pose estimation." Pattern Recognition Letters 154 (February 2022): 1–6. http://dx.doi.org/10.1016/j.patrec.2021.12.018.

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Glasner, Daniel, Meirav Galun, Sharon Alpert, Ronen Basri, and Gregory Shakhnarovich. "Viewpoint-aware object detection and continuous pose estimation." Image and Vision Computing 30, no. 12 (December 2012): 923–33. http://dx.doi.org/10.1016/j.imavis.2012.09.006.

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Zhang, Hongjia, Junwen Huang, Xin Xu, Qiang Fang, and Yifei Shi. "Symmetry-Aware 6D Object Pose Estimation via Multitask Learning." Complexity 2020 (October 21, 2020): 1–7. http://dx.doi.org/10.1155/2020/8820500.

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Although 6D object pose estimation has been intensively explored in the past decades, the performance is still not fully satisfactory, especially when it comes to symmetric objects. In this paper, we study the problem of 6D object pose estimation by leveraging the information of object symmetry. To this end, a network is proposed that predicts 6D object pose and object reflectional symmetry as well as the key points simultaneously via a multitask learning scheme. Consequently, the pose estimation is aware of and regulated by the symmetry axis and the key points of the to-be-estimated objects. Moreover, we devise an optimization function to refine the predicted 6D object pose by considering the predicted symmetry. Experiments on two datasets demonstrate that the proposed symmetry-aware approach outperforms the existing methods in terms of predicting 6D pose estimation of symmetric objects.

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Zou, Xinyi, Guiqing Li, Mengxiao Yin, Yuxin Liu, and Yupan Wang. "Deformation-Graph-Driven and Deformation Aware Spectral Pose Transfer." Journal of Computer-Aided Design & Computer Graphics 33, no. 8 (April 1, 2021): 1234–45. http://dx.doi.org/10.3724/sp.j.1089.2021.18667.

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Zhou, Desen, and Qian He. "PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation." IEEE Access 8 (2020): 15007–16. http://dx.doi.org/10.1109/access.2020.2967147.

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Shen, Mali, Yun Gu, Ning Liu, and Guang-Zhong Yang. "Context-Aware Depth and Pose Estimation for Bronchoscopic Navigation." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 732–39. http://dx.doi.org/10.1109/lra.2019.2893419.

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Більше джерел

Дисертації з теми "Pose-aware"

Gkagkos, Polydefkis. "3D Human Pose and Shape-aware Modelling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285922.

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Анотація:

The focus of this thesis is the task of 3D pose estimation while taking into consideration the shape of a person in a single image. For rendering the human pose and the body shape we use a newly proposed statistical model, the SMPL [1]. We train a neural network to estimate the shape and the pose of a person in an image. Afterwards, we use an optimization procedure to further enhance the output. the network is trained by incorporating the optimized and the predicted parameters into the loss. This approach is based on SPIN [2]. We extend this method by using a stronger optimization that is based on several views and the error is summed over all of them. The main objective of this thesis is to utilize information from multiple views. The motivation for our method is to explore whether this optimization can provide better supervision to the network. In order to verify the effectiveness of our method, we conduct several experiments and we show appealing visual results. Lastly, to make the network generalize better we train simultaneously on seven datasets and achieve comparable to even better accuracy than similar methods from related work.
Fokus för denna avhandling är uppgiften att skatta en mänsklig 3D-pose ochsamtidigt ta hänsyn till personens form i en bild. För att rendera mänskligaposer och kroppsformer använder vi en nyligen föreslagen statistisk modell,SMPL [1]. Vi tränar ett neuralt nätverk för att skatta en persons pose och formi en bild. Därefter använder vi en optimerings-procedur för att ytterligare förbättradessa skattningar. Nätverket tränas genom att integrera de förbättradeskattningarna i en målfunktion tillsammans med de primitiva skattningarna.Denna strategi är baserad på SPIN [2]. Vi utökar denna metod genom att användaen optimerings-procedur som bygger på att inkorporera flera vyer ochsummera felet över alla dessa. Motivationen för vår metod är att utforska omden kan förbättra guidningen av nätverkets träning. För att få vårt nätverk attgeneralisera bättre så tränar vi på sju dataset samtidigt och uppnår jämförbarnoggrannhet med liknande metoder från relaterad forskning. Vi utför även fleraexperiment för att verifiera vår metods effektivitet.

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黃啟清. "Facial Landmark Detection using Pose-Aware Deep Convolutional Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/55011559456482118412.

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碩士
國立清華大學
資訊工程學系
102
Facial landmark detection usually suffers from the influence by the change of environment, such as pose variation and illumination. We observe that high pose variation is the one most influence the detection accuracy. To tackle the problem of pose variation, we adopt deep learning approach to learn a good regressor and propose a pose-aware CNN to tackle the pose variation. We first develop CNN classifier to classify facial image according to the pose. Next, we develop two CNN to detect the facial landmarks according to the corresponding pose. In addition, we adjust the refinement level by concluding the shape constraint. Our experimental results show that the pose-aware detector performs better than the original landmark detector.

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Книги з теми "Pose-aware"

Mendelovici, Angela. Nonconscious States. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190863807.003.0008.

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Nonconscious states, like standing beliefs and nonconscious states involved in early visual processing, pose a challenge for PIT: They seem to be intentional but not phenomenal. This chapter addresses this challenge. It begins by considering versions of PIT that take nonconscious states to have derived intentionality, arguing that none of the suggested derivation mechanisms is up to the task of generating new instances of intentionality. This chapter then recommends an alternative treatment of nonconscious states on which neither standing states nor most nonconscious occurrent states are genuinely intentional, though the self-ascriptivist view described in Chapter 7 might be extended to accommodate some standing state contents, and perhaps even standing states in their entirety. This chapter also suggests that some nonconscious occurrent states might have phenomenal properties we are not aware of and so might have phenomenal intentionality we are also not aware of.

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Campione-Barr, Nicole, Sonia E. Giron, and Christopher Odudu. Relational Aggression in Sibling Relationships. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190491826.003.0014.

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Given the uniqueness of siblings, it is important to consider the presence and role of relational aggression within the sibling relationship. Due to the time spent together and the information disclosed between siblings, during conflict, such information could be used in threatening or relationally aggressive ways. Relationally aggressive actions do not pose a threat to end the sibling relationship, making it a safe relationship to practice such strategies. While parents are likely to be aware of physical aggression between siblings, and attempt to stop it, relational aggression may be difficult for parents to catch and address, reinforcing its effectiveness as a strategy of control. This chapter describes the developmental course of relational aggression within the sibling relationship, as well as associations between relational aggression in the sibling relationship and relationships with others. Finally, we highlight the conclusions and limitations of this research and offer ideas for future research directions.

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Частини книг з теми "Pose-aware"

Hong, Seungpyo, and Jinwook Kim. "Pose-Aware Smoothing Filter for Depth Images." In Advances in Visual Computing, 662–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14364-4_64.

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Zhou, Lu, Yingying Chen, Yunze Gao, Jinqiao Wang, and Hanqing Lu. "Occlusion-Aware Siamese Network for Human Pose Estimation." In Computer Vision – ECCV 2020, 396–412. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_24.

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Zhang, Wenfeng, Zhiqiang Wei, Lei Huang, Jie Nie, Lei Lv, and Guanqun Wei. "Person Re-Identification Based on Pose-Aware Segmentation." In MultiMedia Modeling, 302–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_25.

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Zhao, Mo, and Hao Liu. "Group-Aware Disentangle Learning for Head Pose Estimation." In Pattern Recognition and Computer Vision, 577–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88004-0_47.

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Ke, Lipeng, Ming-Ching Chang, Honggang Qi, and Siwei Lyu. "Multi-Scale Structure-Aware Network for Human Pose Estimation." In Computer Vision – ECCV 2018, 731–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01216-8_44.

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Rhodin, Helge, Mathieu Salzmann, and Pascal Fua. "Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation." In Computer Vision – ECCV 2018, 765–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01249-6_46.

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Sada, Youki, Seiya Shibata, Yuki Kobayashi, and Takashi Takenaka. "RegionDrop: Fast Human Pose Estimation Using Annotation-Aware Spatial Sparsity." In Lecture Notes in Computer Science, 756–68. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_63.

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Lv, Tianqi, Lingrui Wu, Junhua Zhou, Zhonghua Liao, and Xiang Zhai. "Scale-Aware Network with Attentional Selection for Human Pose Estimation." In Human Centered Computing, 326–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70626-5_35.

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Hosseini Jafari, Omid, Siva Karthik Mustikovela, Karl Pertsch, Eric Brachmann, and Carsten Rother. "iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects." In Computer Vision – ACCV 2018, 477–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20893-6_30.

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Ye, Qi, and Tae-Kyun Kim. "Occlusion-Aware Hand Pose Estimation Using Hierarchical Mixture Density Network." In Computer Vision – ECCV 2018, 817–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01249-6_49.

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Тези доповідей конференцій з теми "Pose-aware"

Kumar, Vijay, Anoop Namboodiri, Manohar Paluri, and C. V. Jawahar. "Pose-Aware Person Recognition." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.719.

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Mahajan, Kushagra, Tarasha Khurana, Ayush Chopra, Isha Gupta, Chetan Arora, and Atul Rai. "Pose Aware Fine-Grained Visual Classification Using Pose Experts." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451257.

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Shah, Anshul, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, and Abhinav Shrivastava. "Pose and Joint-Aware Action Recognition." In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2022. http://dx.doi.org/10.1109/wacv51458.2022.00022.

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Gupta, Kartik, Lars Petersson, and Richard Hartley. "CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00337.

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Wang, Junjie, Zhenbo Yu, Zhengyan Tong, Hang Wang, Jinxian Liu, Wenjun Zhang, and Xiaoyan Wu. "OCR-Pose: Occlusion-aware Contrastive Representation for Unsupervised 3D Human Pose Estimation." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3547780.

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Masi, Iacopo, Stephen Rawls, Gerard Medioni, and Prem Natarajan. "Pose-Aware Face Recognition in the Wild." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.523.

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Glasner, Daniel, Meirav Galun, Sharon Alpert, Ronen Basri, and Gregory Shakhnarovich. "Viewpoint-aware object detection and pose estimation." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126379.

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Huang, Siyu, Haoyi Xiong, Zhi-Qi Cheng, Qingzhong Wang, Xingran Zhou, Bihan Wen, Jun Huan, and Dejing Dou. "Generating Person Images with Appearance-aware Pose Stylizer." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/87.

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Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e.g., appearance, pose, foreground, background, local details, global structures, etc. In this paper, we present a novel end-to-end framework to generate realistic person images based on given person poses and appearances. The core of our framework is a novel generator called Appearance-aware Pose Stylizer (APS) which generates human images by coupling the target pose with the conditioned person appearance progressively. The framework is highly flexible and controllable by effectively decoupling various complex person image factors in the encoding phase, followed by re-coupling them in the decoding phase. In addition, we present a new normalization method named adaptive patch normalization, which enables region-specific normalization and shows a good performance when adopted in person image generation model. Experiments on two benchmark datasets show that our method is capable of generating visually appealing and realistic-looking results using arbitrary image and pose inputs.

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Zhang, Feng, Xiatian Zhu, Hanbin Dai, Mao Ye, and Ce Zhu. "Distribution-Aware Coordinate Representation for Human Pose Estimation." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00712.

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Brahmbhatt, Samarth, Heni Ben Amor, and Henrik Christensen. "Occlusion-Aware Object Localization, Segmentation and Pose Estimation." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.80.

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Звіти організацій з теми "Pose-aware"

Microbiology in the 21st Century: Where Are We and Where Are We Going? American Society for Microbiology, 2004. http://dx.doi.org/10.1128/aamcol.5sept.2003.

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The American Academy of Microbiology convened a colloquium September 5–7, 2003, in Charleston, South Carolina to discuss the central importance of microbes to life on earth, directions microbiology research will take in the 21st century, and ways to foster public literacy in this important field. Discussions centered on: the impact of microbes on the health of the planet and its inhabitants; the fundamental significance of microbiology to the study of all life forms; research challenges faced by microbiologists and the barriers to meeting those challenges; the need to integrate microbiology into school and university curricula; and public microbial literacy. This is an exciting time for microbiology. We are becoming increasingly aware that microbes are the basis of the biosphere. They are the ancestors of all living things and the support system for all other forms of life. Paradoxically, certain microbes pose a threat to human health and to the health of plants and animals. As the foundation of the biosphere and major determinants of human health, microbes claim a primary, fundamental role in life on earth. Hence, the study of microbes is pivotal to the study of all living things, and microbiology is essential for the study and understanding of all life on this planet. Microbiology research is changing rapidly. The field has been impacted by events that shape public perceptions of microbes, such as the emergence of globally significant diseases, threats of bioterrorism, increasing failure of formerly effective antibiotics and therapies to treat microbial diseases, and events that contaminate food on a large scale. Microbial research is taking advantage of the technological advancements that have opened new fields of inquiry, particularly in genomics. Basic areas of biological complexity, such as infectious diseases and the engineering of designer microbes for the benefit of society, are especially ripe areas for significant advancement. Overall, emphasis has increased in recent years on the evolution and ecology of microorganisms. Studies are focusing on the linkages between microbes and their phylogenetic origins and between microbes and their habitats. Increasingly, researchers are striving to join together the results of their work, moving to an integration of biological phenomena at all levels. While many areas of the microbiological sciences are ripe for exploration, microbiology must overcome a number of technological hurdles before it can fully accomplish its potential. We are at a unique time when the confluence of technological advances and the explosion of knowledge of microbial diversity will enable significant advances in microbiology, and in biology in general, over the next decade. To make the best progress, microbiology must reach across traditional departmental boundaries and integrate the expertise of scientists in other disciplines. Microbiologists are becoming increasingly aware of the need to harness the vast computing power available and apply it to better advantage in research. Current methods for curating research materials and data should be rethought and revamped. Finally, new facilities should be developed to house powerful research equipment and make it available, on a regional basis, to scientists who might otherwise lack access to the expensive tools of modern biology. It is not enough to accomplish cutting-edge research. We must also educate the children and college students of today, as they will be the researchers of tomorrow. Since microbiology provides exceptional teaching tools and is of pivotal importance to understanding biology, science education in schools should be refocused to include microbiology lessons and lab exercises. At the undergraduate level, a thorough knowledge of microbiology should be made a part of the core curriculum for life science majors. Since issues that deal with microbes have a direct bearing on the human condition, it is critical that the public-at-large become better grounded in the basics of microbiology. Public literacy campaigns must identify the issues to be conveyed and the best avenues for communicating those messages. Decision-makers at federal, state, local, and community levels should be made more aware of the ways that microbiology impacts human life and the ways school curricula could be improved to include valuable lessons in microbial science.

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