Computer Vision
AR/VR
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
August 21, 2020
Abstract
We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGBD video. TexMesh enables high quality free-viewpoint rendering of humans. Given the RGB frames, the captured environment map, and the coarse per-frame human mesh from RGB-D tracking, our method reconstructs spatiotemporally consistent and detailed per-frame meshes along with a high-resolution albedo texture. By using the incident illumination we are able to accurately estimate local surface geometry and albedo, which allows us to further use photometric constraints to adapt a synthetically trained model to real-world sequences in a self-supervised manner for detailed surface geometry and high-resolution texture estimation. In practice, we train our models on a short example sequence for self-adaptation and the model runs at interactive framerate afterwards. We validate TexMesh on synthetic and real-world data, and show it outperforms the state of art quantitatively and qualitatively.
AUTHORS
Written by
Tiancheng Zhi
Christoph Lassner
Tony Tung
Carsten Stoll
Srinivasa G. Narasimhan
Minh Vo
Publisher
European Conference on Computer Vision (ECCV)
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