1 research outputs found
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
We present TexMesh, a novel approach to reconstruct detailed human meshes
with high-resolution full-body texture from RGB-D 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.Comment: ECCV 202