Search Results for author: Marc Habermann

Found 24 papers, 3 papers with code

NeuralClothSim: Neural Deformation Fields Meet the Kirchhoff-Love Thin Shell Theory

no code implementations24 Aug 2023 Navami Kairanda, Marc Habermann, Christian Theobalt, Vladislav Golyanik

Cloth simulation is an extensively studied problem, with a plethora of solutions available in computer graphics literature.

ROAM: Robust and Object-aware Motion Generation using Neural Pose Descriptors

no code implementations24 Aug 2023 Wanyue Zhang, Rishabh Dabral, Thomas Leimkühler, Vladislav Golyanik, Marc Habermann, Christian Theobalt

Given an unseen object and a reference pose-object pair, we optimise for the object-aware pose that is closest in the feature space to the reference pose.

Motion Synthesis

VINECS: Video-based Neural Character Skinning

no code implementations3 Jul 2023 Zhouyingcheng Liao, Vladislav Golyanik, Marc Habermann, Christian Theobalt

However, the former methods typically predict solely static skinning weights, which perform poorly for highly articulated poses, and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time.

EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors

no code implementations2 May 2023 Xinyu Yi, Yuxiao Zhou, Marc Habermann, Vladislav Golyanik, Shaohua Pan, Christian Theobalt, Feng Xu

We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera.

Simultaneous Localization and Mapping

Scene-Aware 3D Multi-Human Motion Capture from a Single Camera

1 code implementation12 Jan 2023 Diogo Luvizon, Marc Habermann, Vladislav Golyanik, Adam Kortylewski, Christian Theobalt

In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera.

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

1 code implementation10 Dec 2022 Yiming Wang, Qin Han, Marc Habermann, Kostas Daniilidis, Christian Theobalt, Lingjie Liu

Recent methods for neural surface representation and rendering, for example NeuS, have demonstrated the remarkably high-quality reconstruction of static scenes.

Surface Reconstruction

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

no code implementations27 Oct 2022 Edith Tretschk, Navami Kairanda, Mallikarjun B R, Rishabh Dabral, Adam Kortylewski, Bernhard Egger, Marc Habermann, Pascal Fua, Christian Theobalt, Vladislav Golyanik

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics.

3D Reconstruction

HDHumans: A Hybrid Approach for High-fidelity Digital Humans

no code implementations21 Oct 2022 Marc Habermann, Lingjie Liu, Weipeng Xu, Gerard Pons-Moll, Michael Zollhoefer, Christian Theobalt

Photo-real digital human avatars are of enormous importance in graphics, as they enable immersive communication over the globe, improve gaming and entertainment experiences, and can be particularly beneficial for AR and VR settings.

Novel View Synthesis Surface Reconstruction +1

HiFECap: Monocular High-Fidelity and Expressive Capture of Human Performances

no code implementations11 Oct 2022 Yue Jiang, Marc Habermann, Vladislav Golyanik, Christian Theobalt

Furthermore, we show that HiFECap outperforms the state-of-the-art human performance capture approaches qualitatively and quantitatively while for the first time capturing all aspects of the human.

Vocal Bursts Intensity Prediction

Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination

no code implementations27 Jul 2022 Linjie Lyu, Ayush Tewari, Thomas Leimkuehler, Marc Habermann, Christian Theobalt

Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics.

Disentanglement Novel View Synthesis

A Deeper Look into DeepCap

no code implementations20 Nov 2021 Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.

Pose Estimation

NRST: Non-rigid Surface Tracking from Monocular Video

no code implementations6 Jul 2021 Marc Habermann, Weipeng Xu, Helge Rhodin, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

Our texture term exploits the orientation information in the micro-structures of the objects, e. g., the yarn patterns of fabrics.

Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control

no code implementations3 Jun 2021 Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, Christian Theobalt

To address this problem, we utilize a coarse body model as the proxy to unwarp the surrounding 3D space into a canonical pose.

Real-time Deep Dynamic Characters

no code implementations4 May 2021 Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery.

Monocular Real-time Full Body Capture with Inter-part Correlations

no code implementations CVPR 2021 Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie, Ayush Tewari, Christian Theobalt, Feng Xu

We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image.

3D Hand Pose Estimation Face Model

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

no code implementations25 Nov 2020 Yue Li, Marc Habermann, Bernhard Thomaszewski, Stelian Coros, Thabo Beeler, Christian Theobalt

Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.

Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data

2 code implementations CVPR 2020 Yuxiao Zhou, Marc Habermann, Weipeng Xu, Ikhsanul Habibie, Christian Theobalt, Feng Xu

We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy.

Pose Estimation

DeepCap: Monocular Human Performance Capture Using Weak Supervision

no code implementations CVPR 2020 Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.

Pose Estimation

Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation

no code implementations14 Jan 2020 Lingjie Liu, Weipeng Xu, Marc Habermann, Michael Zollhoefer, Florian Bernard, Hyeongwoo Kim, Wenping Wang, Christian Theobalt

In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space.

Image-to-Image Translation Novel View Synthesis +1

Neural Rendering and Reenactment of Human Actor Videos

no code implementations11 Sep 2018 Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Hyeongwoo Kim, Florian Bernard, Marc Habermann, Wenping Wang, Christian Theobalt

In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person.

Image Generation Neural Rendering

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