  Neural implicit surface representations have recently emerged as popular
alternative to explicit 3D object encodings, such as polygonal meshes,
tabulated points, or voxels. While significant work has improved the geometric
fidelity of these representations, much less attention is given to their final
appearance. Traditional explicit object representations commonly couple the 3D
shape data with auxiliary surface-mapped image data, such as diffuse color
textures and fine-scale geometric details in normal maps that typically require
a mapping of the 3D surface onto a plane, i.e., a surface parameterization;
implicit representations, on the other hand, cannot be easily textured due to
lack of configurable surface parameterization. Inspired by this digital content
authoring methodology, we design a neural network architecture that implicitly
encodes the underlying surface parameterization suitable for appearance data.
As such, our model remains compatible with existing mesh-based digital content
with appearance data. Motivated by recent work that overfits compact networks
to individual 3D objects, we present a new weight-encoded neural implicit
representation that extends the capability of neural implicit surfaces to
enable various common and important applications of texture mapping. Our method
outperforms reasonable baselines and state-of-the-art alternatives.
