  Remarkable advances have been achieved recently in learning neural
representations that characterize object geometry, while generating textured
objects suitable for downstream applications and 3D rendering remains at an
early stage. In particular, reconstructing textured geometry from images of
real objects is a significant challenge -- reconstructed geometry is often
inexact, making realistic texturing a significant challenge. We present
Mesh2Tex, which learns a realistic object texture manifold from uncorrelated
collections of 3D object geometry and photorealistic RGB images, by leveraging
a hybrid mesh-neural-field texture representation. Our texture representation
enables compact encoding of high-resolution textures as a neural field in the
barycentric coordinate system of the mesh faces. The learned texture manifold
enables effective navigation to generate an object texture for a given 3D
object geometry that matches to an input RGB image, which maintains robustness
even under challenging real-world scenarios where the mesh geometry
approximates an inexact match to the underlying geometry in the RGB image.
Mesh2Tex can effectively generate realistic object textures for an object mesh
to match real images observations towards digitization of real environments,
significantly improving over previous state of the art.
