Search Results for author: Soshi Shimada

Found 10 papers, 0 papers with code

HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance

no code implementations11 May 2022 Soshi Shimada, Vladislav Golyanik, 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.

HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks

no code implementations2 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.

3D Hand Pose Estimation

Fast Simultaneous Gravitational Alignment of Multiple Point Sets

no code implementations21 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.

Neural Monocular 3D Human Motion Capture with Physical Awareness

no code implementations3 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.

3D Pose Estimation

PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time

no code implementations20 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.

DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies

no code implementations24 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.

IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction

no code implementations27 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.

3D Reconstruction

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

no code implementations27 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.

3D Reconstruction

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