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Regression of pixel locations within a normalized UV map to determine positions on a 3D mesh.
Distinct from Coordinate Regression: Specifically addresses the regression of UV map coordinates for surface mapping, not general spatial coordinate regression.
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- facebookresearch/densepose
facebookresearch/DensePose
7,252Auf GitHub ansehenDensePose is a 3D human pose estimation framework designed to map 2D image pixels to a 3D surface-based model of the human body in real time. It functions as a computer vision anatomical mapper that projects 2D visual data onto a 3D surface to create detailed anatomical representations. The system operates as an image-to-3D texture transfer engine, localizing 2D image annotations onto 3D models to apply photographic textures to digital human representations. It uses a surface-based body mapping method to associate human pixels in an RGB image with specific coordinates on a 3D body template.
Predicts exact pixel locations on a 3D mesh by regressing values within a normalized UV map.
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