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Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture [article]

Yue Li, Marc Habermann, Bernhard Thomaszewski, Stelian Coros, Thabo Beeler, Christian Theobalt
2021 arXiv   pre-print
of weakly supervised deep monocular human performance capture.  ...  capture of the entire deforming surface of a clothed human.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for their valuable feedback, and Gereon Fox for the video narration.  ... 
arXiv:2011.12866v2 fatcat:unwkgbplznatfjr2t66zt644dq

A Deeper Look into DeepCap [article]

Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt
2021 arXiv   pre-print
We propose a novel deep learning approach for monocular dense human performance capture.  ...  Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.  ...  ACKNOWLEDGMENT This work was funded by the ERC Consolidator Grant 4DRepLy (770784) and the Deutsche Forschungsgemeinschaft (Project Nr. 409792180, Emmy Noether Programme, project: Real Virtual Humans).  ... 
arXiv:2111.10563v1 fatcat:pkwf5736rje43ihu7pmkjjggly

Reconstruction of People in Loose Clothing [article]

Sai Sagar Jinka, Rohan Chacko, Astitva Srivastava, Avinash Sharma, P.J. Narayanan
2021 arXiv   pre-print
3D human body reconstruction from monocular images is an interesting and ill-posed problem in computer vision with wider applications in multiple domains.  ...  In this paper, we propose SHARP, a novel end-to-end trainable network that accurately recovers the detailed geometry and appearance of 3D people in loose clothing from a monocular image.  ...  Conclusion We introduced a novel shape-aware peeled representation for the reconstruction of human bodies with loose clothing.  ... 
arXiv:2106.04778v3 fatcat:5as3byi76ve5tez6qzio3mwb7y

M3D-VTON: A Monocular-to-3D Virtual Try-On Network [article]

Fuwei Zhao, Zhenyu Xie, Michael Kampffmeyer, Haoye Dong, Songfang Han, Tianxiang Zheng, Tao Zhang, Xiaodan Liang
2021 arXiv   pre-print
In this paper, we propose a novel Monocular-to-3D Virtual Try-On Network (M3D-VTON) that builds on the merits of both 2D and 3D approaches.  ...  a faster alternative to manipulate clothed humans, but lack the rich and realistic 3D representation.  ...  Compared to the tasks of 3D human reconstruction and performance capturing [54, 14, 11, 39, 53, 24, 36, 26, 21, 1] , 3D virtual try-on is more challenging due to the complex deformation of clothes.  ... 
arXiv:2108.05126v1 fatcat:ynvnzx7y7fgjncxm4urwll7hiy

Deep Learning-Based Human Pose Estimation: A Survey [article]

Ce Zheng and Wenhan Wu and Chen Chen and Taojiannan Yang and Sijie Zhu and Ju Shen and Nasser Kehtarnavaz and Mubarak Shah
2022 arXiv   pre-print
The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions  ...  Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities  ...  Clothes parsing [270] [210] and pose transfer [129] make it possible by inferring the 3D appearance of a person wearing a specific clothes.  ... 
arXiv:2012.13392v4 fatcat:ypnqtq3sbncr5fuujif2dhqwji

Recovering 3D Human Mesh from Monocular Images: A Survey [article]

Yating Tian, Hongwen Zhang, Yebin Liu, Limin Wang
2022 arXiv   pre-print
To the best of our knowledge, this is the first survey to focus on the task of monocular 3D human mesh recovery.  ...  Estimating human pose and shape from monocular images is a long-standing problem in computer vision.  ...  Scope This survey mainly focuses on approaches to monocular 3D human mesh recovery in the deep learning era.  ... 
arXiv:2203.01923v2 fatcat:vb6xa5wdsrhdxd2ebvg54qq2m4

3D Human Shape Reconstruction from a Polarization Image [article]

Shihao Zou, Xinxin Zuo, Yiming Qian, Sen Wang, Chi Xu, Minglun Gong, Li Cheng
2020 arXiv   pre-print
This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images.  ...  shape of clothing details.  ...  Acknowledgement This work is supported by the NSERC Discovery Grants, and the University of Alberta-Huawei Joint Innovation Collaboration grants.  ... 
arXiv:2007.09268v1 fatcat:c2trusxcyjfs5i6hf2kac4oetu

HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model [article]

Vladislav Golyanik and Soshi Shimada and Kiran Varanasi and Didier Stricker
2019 arXiv   pre-print
In this work, we propose a new hybrid approach for monocular non-rigid reconstruction which we call Hybrid Deformation Model Network (HDM-Net).  ...  Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision.  ...  We propose the first, to the best of our knowledge, deep neural network (DNN) based deformation model for MNR.  ... 
arXiv:1803.10193v2 fatcat:szrra6fw6vat5m7y7gjulzoiya

3D Morphable Models (Dagstuhl Seminar 19102)

Bernhard Egger, William Smith, Christian Theobalt, Thomas Vetter, Michael Wagner
2019 Dagstuhl Reports  
It was a first specific meeting of a broader group of people working with 3D Morphable Models of faces and bodies.  ...  This meeting of 26 researchers was held 20 years after the seminal work was published at Siggraph. We summarize the discussions, presentations and results of this workshop.  ...  Using our work on monocular face performance capture, I discuss several classes of algorithms developed for models of the world in motion from a single color camera.  ... 
doi:10.4230/dagrep.9.3.16 dblp:journals/dagstuhl-reports/EggerSTV19 fatcat:rt7gzigv3rfhjez2iswdejjpwa

VR content creation and exploration with deep learning: A survey

Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang
2020 Computational Visual Media  
This article surveys recent research that uses such deep learning methods for VR content creation and exploration.  ...  Intelligence of VR methods and applications has been significantly boosted by the recent developments in deep learning techniques.  ...  This work was supported by the National Natural Science Foundation of China (Grant Nos. 61902012, 61932003). Fang-Lue Zhang was supported by a Victoria Early-Career Research Excellence Award.  ... 
doi:10.1007/s41095-020-0162-z fatcat:lgogzx26bvhn5f7uyefjkz7zny

Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction [article]

Tong He, John Collomosse, Hailin Jin, Stefano Soatto
2020 arXiv   pre-print
We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color image of a clothed person.  ...  We show that, by both encoding query points and constraining global shape using latent voxel features, the reconstruction we obtain for clothed human meshes exhibits less shape distortion and improved  ...  Recently, deep implicit modeling techniques delivered a step change in 3D reconstruction of clothed human meshes from monocular images.  ... 
arXiv:2006.08072v2 fatcat:7qbnswptdfapzh2e5bxygvrsgy

Modern Augmented Reality: Applications, Trends, and Future Directions [article]

Shervin Minaee, Xiaodan Liang, Shuicheng Yan
2022 arXiv   pre-print
We then give an overview of around 100 recent promising machine learning based works developed for AR systems, such as deep learning works for AR shopping (clothing, makeup), AR based image filters (such  ...  Although it has been around for nearly fifty years, it has seen a lot of interest by the research community in the recent years, mainly because of the huge success of deep learning models for various computer  ...  ACKNOWLEDGMENTS We would like to thank Iasonas Kokkinos, Qi Pan, Lyric Kaplan, and Liz Markman for reviewing this work, and providing very helpful comments and suggestions.  ... 
arXiv:2202.09450v2 fatcat:x436ycnvxnhdpfdvhnxkzgbqce

Data-Driven 3D Reconstruction of Dressed Humans From Sparse Views [article]

Pierre Zins, Yuanlu Xu, Edmond Boyer, Stefanie Wuhrer, Tony Tung
2021 arXiv   pre-print
We propose a data-driven end-to-end approach that reconstructs an implicit 3D representation of dressed humans from sparse camera views.  ...  Specifically, we introduce three key components: first a spatially consistent reconstruction that allows for arbitrary placement of the person in the input views using a perspective camera model; second  ...  Acknowledgements We thank Laurence Boissieux and Julien Pansiot from the Kinovis platform at Inria Grenoble and our volunteer subjects for help with the 3D data acquisition.  ... 
arXiv:2104.08013v4 fatcat:vcvpmekukbehndo7iy7muv2yoa

Real-time Deep Dynamic Characters [article]

Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt
2021 arXiv   pre-print
During training, we do not need to resort to difficult dynamic 3D capture of the human; instead we can train our model entirely from multi-view video in a weakly supervised manner.  ...  We show that by merely providing new skeletal motions, our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures  ...  ACKNOWLEDGMENTS All data captures and evaluations were performed at MPII by MPII.  ... 
arXiv:2105.01794v1 fatcat:q34njck5pffd3lryvzk2psxmoq

Monocular human pose estimation: A survey of deep learning-based methods

Yucheng Chen, Yingli Tian, Mingyi He
2020 Computer Vision and Image Understanding  
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences.  ...  The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation.  ...  Yucheng Chen's contribution was made when he was a visiting student at the City University of New York, sponsored by the Chinese Scholarship Council.  ... 
doi:10.1016/j.cviu.2019.102897 fatcat:7gf3exlfgrax7dnxrmv2pphzvy
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