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SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
We present SPSG, a novel approach to generate high-quality, colored 3D m...
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Ear-to-ear Capture of Facial Intrinsics
We present a practical approach to capturing ear-to-ear face models comp...
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LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
Motivated by augmented and virtual reality applications such as telepres...
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TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
We introduce, TextureNet, a neural network architecture designed to extr...
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Neural Subdivision
This paper introduces Neural Subdivision, a novel framework for data-dri...
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High-resolution computer meshes of the lower body bones of an adult human female derived from CT images
Background Computer-based geometrical meshes of bones are important for ...
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Is a Green Screen Really Necessary for Real-Time Human Matting?
For human matting without the green screen, existing works either requir...
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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.
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