22 citations · 38 across the 15 of their papers we have counts for
17 papers
DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image
Qingxuan Wu, Zhiyang Dou, Sirui Xu +11
Reconstructing 3D hand-face interactions with deformations from a single image is a challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges stem…
MACS: Mass Conditioned 3D Hand and Object Motion Synthesis
Soshi Shimada, Franziska Mueller, Jan Bednarik +7
The physical properties of an object, such as mass, significantly affect how we manipulate it with our hands. Surprisingly, this aspect has so far been neglected in prior work on 3…
Decaf: Monocular Deformation Capture for Face and Hand Interactions
Soshi Shimada, Vladislav Golyanik, Patrick Pérez +1
Existing methods for 3D tracking from monocular RGB videos predominantly consider articulated and rigid objects. Modelling dense non-rigid object deformations in this setting remai…
MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes
Zhi Li, Soshi Shimada, Bernt Schiele +2
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-…
UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture
Hiroyasu Akada, Jian Wang, Soshi Shimada +3
We present UnrealEgo, i.e., a new large-scale naturalistic dataset for egocentric 3D human pose estimation. UnrealEgo is based on an advanced concept of eyeglasses equipped with tw…
Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model
Erik C. M. Johnson, Marc Habermann, Soshi Shimada +2
Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications. However, current approaches suffer from drawbacks such as…
HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance
Soshi Shimada, Vladislav Golyanik, Zhi Li +3
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.…
Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors
Xinyu Yi, Yuxiao Zhou, Marc Habermann +4
Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recordin…
Gravity-Aware Monocular 3D Human-Object Reconstruction
Rishabh Dabral, Soshi Shimada, Arjun Jain +2
This paper proposes GraviCap, i.e., a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos. We focus on scenes with…
HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks
Jameel Malik, Soshi Shimada, Ahmed Elhayek +4
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress h…
Fast Simultaneous Gravitational Alignment of Multiple Point Sets
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 rel…
Neural Monocular 3D Human Motion Capture with Physical Awareness
Soshi Shimada, Vladislav Golyanik, Weipeng Xu +2
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. Un…
PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time
Soshi Shimada, Vladislav Golyanik, Weipeng Xu +1
Marker-less 3D human motion capture from a single colour camera has seen significant progress. However, it is a very challenging and severely ill-posed problem. In consequence, eve…
HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map
Jameel Malik, Ibrahim Abdelaziz, Ahmed Elhayek +5
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly regress 3D…
DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies
Soshi Shimada, Vladislav Golyanik, Edgar Tretschk +2
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…
IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction
Soshi Shimada, Vladislav Golyanik, Christian Theobalt +1
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 a…
HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model
Vladislav Golyanik, Soshi Shimada, Kiran Varanasi +1
Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision. Current techniques either require dense correspondences and rely on motion a…