  In recent years, substantial progress has been achieved in learning-based
reconstruction of 3D objects. At the same time, generative models were proposed
that can generate highly realistic images. However, despite this success in
these closely related tasks, texture reconstruction of 3D objects has received
little attention from the research community and state-of-the-art methods are
either limited to comparably low resolution or constrained experimental setups.
A major reason for these limitations is that common representations of texture
are inefficient or hard to interface for modern deep learning techniques. In
this paper, we propose Texture Fields, a novel texture representation which is
based on regressing a continuous 3D function parameterized with a neural
network. Our approach circumvents limiting factors like shape discretization
and parameterization, as the proposed texture representation is independent of
the shape representation of the 3D object. We show that Texture Fields are able
to represent high frequency texture and naturally blend with modern deep
learning techniques. Experimentally, we find that Texture Fields compare
favorably to state-of-the-art methods for conditional texture reconstruction of
3D objects and enable learning of probabilistic generative models for texturing
unseen 3D models. We believe that Texture Fields will become an important
building block for the next generation of generative 3D models.
