  We present a novel approach for single-image mesh texturing, which employs a
diffusion model with judicious conditioning to seamlessly transfer an object's
texture from a single RGB image to a given 3D mesh object. We do not assume
that the two objects belong to the same category, and even if they do, there
can be significant discrepancies in their geometry and part proportions. Our
method aims to rectify the discrepancies by conditioning a pre-trained Stable
Diffusion generator with edges describing the mesh through ControlNet, and
features extracted from the input image using IP-Adapter to generate textures
that respect the underlying geometry of the mesh and the input texture without
any optimization or training. We also introduce Image Inversion, a novel
technique to quickly personalize the diffusion model for a single concept using
a single image, for cases where the pre-trained IP-Adapter falls short in
capturing all the details from the input image faithfully. Experimental results
demonstrate the efficiency and effectiveness of our edge-aware single-image
mesh texturing approach, coined EASI-Tex, in preserving the details of the
input texture on diverse 3D objects, while respecting their geometry.
