118 citations · 275 across the 10 of their papers we have counts for
5 papers · 1 filter
A Deeper Look into DeepCap
Marc Habermann, Weipeng Xu, Michael Zollhoefer +2
Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture…
Real-time Deep Dynamic Characters
Marc Habermann, Lingjie Liu, Weipeng Xu +3
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 i…
Mixture of Volumetric Primitives for Efficient Neural Rendering
Stephen Lombardi, Tomas Simon, Gabriel Schwartz +3
Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications. Existing methods have a number of drawbacks we aim to address with o…
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
Shih-Yang Su, Frank Yu, Michael Zollhoefer +1
While deep learning reshaped the classical motion capture pipeline with feed-forward networks, generative models are required to recover fine alignment via iterative refinement. Un…
PVA: Pixel-aligned Volumetric Avatars
Amit Raj, Michael Zollhoefer, Tomas Simon +4
Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality…