Search Results for author: Marc Habermann

Found 13 papers, 1 papers with code

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.

Frame 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.

Frame

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.

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.

Frame 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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