Texture transfer and synthesis using aligned maps in image generation systems and applications

Abstract

Approaches presented herein can utilize a network that learns to embed three-dimensional (3D) coordinates on a surface of one or more 3D shapes into an aligned two-dimensional (2D) texture space, where corresponding parts of different 3D shapes can be mapped to the same location in a texture image. Alignment can be performed using a texture alignment module that generates a set of basis images for synthesizing textures. A trained network can generate a basis shared by all shape textures, and can predict input-specific coefficients to construct the output texture for each shape as a linear combination of the basis images, then deform the texture to match the pose of the input. Such an approach can ensure alignment of textures, even in situations with at least somewhat limited network capacity. To unwrap shapes of complex structure or topology, a masking network can be utilized that cuts the shape into multiple pieces to reduce the distortion in the 2D mapping.