no code implementations • 22 Dec 2023 • Soshi Shimada, Franziska Mueller, Jan Bednarik, Bardia Doosti, Bernd Bickel, Danhang Tang, Vladislav Golyanik, Jonathan Taylor, Christian Theobalt, Thabo Beeler
To improve the naturalness of the synthesized 3D hand object motions, this work proposes MACS the first MAss Conditioned 3D hand and object motion Synthesis approach.
no code implementations • 28 Sep 2023 • Soshi Shimada, Vladislav Golyanik, Patrick Pérez, Christian Theobalt
At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations.
1 code implementation • 17 Aug 2022 • Zhi Li, Soshi Shimada, Bernt Schiele, Christian Theobalt, Vladislav Golyanik
3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem.
no code implementations • 2 Aug 2022 • Hiroyasu Akada, Jian Wang, Soshi Shimada, Masaki Takahashi, Christian Theobalt, Vladislav Golyanik
We present UnrealEgo, i. e., a new large-scale naturalistic dataset for egocentric 3D human pose estimation.
Ranked #4 on Egocentric Pose Estimation on UnrealEgo
no code implementations • 16 Jun 2022 • Erik C. M. Johnson, Marc Habermann, Soshi Shimada, Vladislav Golyanik, Christian Theobalt
Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications.
no code implementations • 11 May 2022 • Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, Christian Theobalt
Marker-less monocular 3D human motion capture (MoCap) with scene interactions is a challenging research topic relevant for extended reality, robotics and virtual avatar generation.
no code implementations • ICCV 2021 • Rishabh Dabral, Soshi Shimada, Arjun Jain, Christian Theobalt, Vladislav Golyanik
We evaluate GraviCap on a new dataset with ground-truth annotations for persons and different objects undergoing free flights.
no code implementations • 2 Jul 2021 • Jameel Malik, Soshi Shimada, Ahmed Elhayek, Sk Aziz Ali, Christian Theobalt, Vladislav Golyanik, Didier Stricker
To address the limitations of the existing methods, we develop HandVoxNet++, i. e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner.
no code implementations • 21 Jun 2021 • Vladislav Golyanik, Soshi Shimada, Christian Theobalt
The problem of simultaneous rigid alignment of multiple unordered point sets which is unbiased towards any of the inputs has recently attracted increasing interest, and several reliable methods have been newly proposed.
no code implementations • 3 May 2021 • Soshi Shimada, Vladislav Golyanik, Weipeng Xu, Patrick Pérez, Christian Theobalt
We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios.
no code implementations • 20 Aug 2020 • Soshi Shimada, Vladislav Golyanik, Weipeng Xu, Christian Theobalt
We, therefore, present PhysCap, the first algorithm for physically plausible, real-time and marker-less human 3D motion capture with a single colour camera at 25 fps.
no code implementations • CVPR 2020 • Jameel Malik, Ibrahim Abdelaziz, Ahmed Elhayek, Soshi Shimada, Sk Aziz Ali, Vladislav Golyanik, Christian Theobalt, Didier Stricker
The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations.
no code implementations • 24 Jul 2019 • Soshi Shimada, Vladislav Golyanik, Edgar Tretschk, Didier Stricker, Christian Theobalt
We introduce a supervised-learning framework for non-rigid point set alignment of a new kind - Displacements on Voxels Networks (DispVoxNets) - which abstracts away from the point set representation and regresses 3D displacement fields on regularly sampled proxy 3D voxel grids.
no code implementations • 27 Apr 2019 • Soshi Shimada, Vladislav Golyanik, Christian Theobalt, Didier Stricker
The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates.
no code implementations • 27 Mar 2018 • Vladislav Golyanik, Soshi Shimada, Kiran varanasi, Didier Stricker
Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision.