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LiveCap: Real-time Human Performance Capture from Monocular Video [article]

Marc Habermann and Weipeng Xu and Michael Zollhoefer and Gerard Pons-Moll and Christian Theobalt
2019 arXiv   pre-print
We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video  ...  Our method is the first real-time monocular approach for full-body performance capture.  ...  Our approach captures high-quality models of humans and their clothing in real-time from a single monocular camera.  ... 
arXiv:1810.02648v3 fatcat:ijfxnvdczzc6jh2gpywnmde47y

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
Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.  ...  of weakly supervised deep monocular human performance capture.  ...  The authors from MPII were supported by the ERC Consolidator Grant 4DRepLy (770784).  ... 
arXiv:2011.12866v2 fatcat:unwkgbplznatfjr2t66zt644dq

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

Yue Li, Marc Habermann, Bernhard Thomaszewski, Stelian Coros, Thabo Beeler, Christian Theobalt
2021 2021 International Conference on 3D Vision (3DV)  
Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.  ...  in the context of weakly-supervised deep monocular human performance capture.  ...  Conclusion We propose a physics-aware deep learning-based method for monocular human performance capture.  ... 
doi:10.1109/3dv53792.2021.00047 fatcat:ddbya2wsczfntmt3hi45dnvrqa

DeepCap: Monocular Human Performance Capture Using Weak Supervision

Marc Habermann, Weipeng Xu, Michael Zollhofer, Gerard Pons-Moll, Christian Theobalt
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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.  ...  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).  ... 
doi:10.1109/cvpr42600.2020.00510 dblp:conf/cvpr/HabermannXZPT20 fatcat:653nddvgfbckxepgpsthfvoipq

DeepCap: Monocular Human Performance Capture Using Weak Supervision [article]

Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt
2020 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.  ...  In particular for the latter applications, being able to performance capture humans from monocular video would be a game changer.  ... 
arXiv:2003.08325v1 fatcat:3sb7icxkhvhbteftru3a6kg27y

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

Monocular Real-Time Volumetric Performance Capture [article]

Ruilong Li, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, Hao Li
2020 arXiv   pre-print
We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view systems or cumbersome pre-acquisition  ...  We also show that our approach compares favorably with the state-of-the-art monocular performance capture.  ...  .: Livecap: Real-time human performance capture from monocular video. ACM Transactions on Graphics (TOG) 38(2), 14 (2019) 19. HDRI Haven: (2018), https://hdrihaven.com/ 20.  ... 
arXiv:2007.13988v1 fatcat:wkf4msosnncozlkt2a23tdr3q4

SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video [article]

Boyi Jiang, Yang Hong, Hujun Bao, Juyong Zhang
2022 arXiv   pre-print
We propose SelfRecon, a clothed human body reconstruction method that combines implicit and explicit representations to recover space-time coherent geometries from a monocular self-rotating human video  ...  Extensive experimental results demonstrate its effectiveness on real captured monocular videos. The source code is available at https://github.com/jby1993/SelfReconCode.  ...  LiveCap [17] refines the optimization pipeline and achieves real-time tracking for a specific person with monocular RGB input.  ... 
arXiv:2201.12792v2 fatcat:ocubpi7ug5glxonubsl7crluhe

AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture [article]

Zhe Li, Zerong Zheng, Hongwen Zhang, Chaonan Ji, Yebin Liu
2022 arXiv   pre-print
To address the ill-posed problem caused by partial observations in monocular human volumetric capture, we present AvatarCap, a novel framework that introduces animatable avatars into the capture pipeline  ...  Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly recon-structs high-fidelity 3D textured models  ...  LiveCap [20] and DeepCap [21] jointly solved or inferred both skeletal and non-rigid motions from a monocular RGB video.  ... 
arXiv:2207.02031v2 fatcat:2zo6wlwezrdtzbnv3hyd643qae

MVP-Human Dataset for 3D Human Avatar Reconstruction from Unconstrained Frames [article]

Xiangyu Zhu, Tingting Liao, Jiangjing Lyu, Xiang Yan, Yunfeng Wang, Kan Guo, Qiong Cao, Stan Z. Li, Zhen Lei
2022 arXiv   pre-print
In this paper, we consider a novel problem of reconstructing a 3D human avatar from multiple unconstrained frames, independent of assumptions on camera calibration, capture space, and constrained actions  ...  Overall, benefits from the specific network architecture and the diverse data, the trained model enables 3D avatar reconstruction from unconstrained frames and achieves state-of-the-art performance.  ...  Recently, some methods try to recover a clothed 3D avatar from a monocular video in which a person is moving.  ... 
arXiv:2204.11184v1 fatcat:etohs6biivcsvjgzwwrjbxj4qi

HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge [article]

Jae Shin Yoon, Zhixuan Yu, Jaesik Park, Hyun Soo Park
2021 arXiv   pre-print
are used to capture 772 distinctive subjects across gender, ethnicity, age, and style.  ...  The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of five primary body signals including gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras  ...  Pons-Moll, and social interaction capture. TPAMI, 2017. 3 C. Theobalt. Livecap: Real-time human performance capture [42] H. Joo, T. Simon, and Y. Sheikh.  ... 
arXiv:2110.00119v2 fatcat:bakqd343fzfonl3sv2f4zw4rra

Reconstructing NBA Players [article]

Luyang Zhu, Konstantinos Rematas, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman
2020 arXiv   pre-print
Key to our approach is a new method for creating poseable, skinned models of NBA players, and a large database of meshes (derived from the NBA2K19 video game), that we are releasing to the research community  ...  Great progress has been made in 3D body pose and shape estimation from a single photo.  ...  We thank Visual Concepts for allowing us to capture, process, and share NBA2K19 data for research.  ... 
arXiv:2007.13303v1 fatcat:nfc5rbbsezbwjhand3vcrobf7e

Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction [article]

Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll
2021 arXiv   pre-print
Our models and code can be downloaded from http://virtualhumans.mpi-inf.mpg.de/ipnet.  ...  We subsequently use correspondences to fit the body model to our inner surface and then non-rigidly deform it (under a parametric body + displacement model) to the outer surface in order to capture garment  ...  Habermann, M., Xu, W., Zollhöfer, M., Pons-Moll, G., Theobalt, C.: Livecap: Real-time human performance capture from monocular video. ACM Trans.  ... 
arXiv:2007.11432v2 fatcat:mtcxrvdszjc4fhfo7gztc74gqq

Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images [article]

Liguo Jiang, Miaopeng Li, Jianjie Zhang, Congyi Wang, Juntao Ye, Xinguo Liu, Jinxiang Chai
2021 arXiv   pre-print
We demonstrate the results of our realtime 3D pose and human body reconstruction system on various challenging in-the-wild videos.  ...  We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime.  ...  More importantly, VNect gets global coordinates rather than rootrelative coordinates, and produces real-time performance.  ... 
arXiv:2106.11536v1 fatcat:2qdgraw3djbxfgidaknnfuv37e

Dynamic Surface Function Networks for Clothed Human Bodies [article]

Andrei Burov and Matthias Nießner and Justus Thies
2021 arXiv   pre-print
We present a novel method for temporal coherent reconstruction and tracking of clothed humans.  ...  As a result, we are able to reconstruct a temporally coherent mesh sequence from the input data.  ...  Note that our method is not designed for real-time use such as LiveCap [22] , since our focus lies on the global reconstruction of a controllable mesh with a pose-dependent sur-face.  ... 
arXiv:2104.03978v2 fatcat:semjaliw6fayfl7d6u3ixa7kqy
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