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We introduce a framework for intrinsic latent diffusion models operatingdirectly on the surfaces of 3D shapes, with the goal of synthesizinghigh-quality textures. Our approach is underpinned by two contributions: fieldlatents, a latent representation encoding textures as discrete vector fields onthe mesh vertices, and field latent diffusion models, which learn to denoise adiffusion process in the learned latent space on the surface. We consider asingle-textured-mesh paradigm, where our models are trained to generatevariations of a given texture on a mesh. We show the synthesized textures areof superior fidelity compared those from existing single-textured-meshgenerative models. Our models can also be adapted for user-controlled editingtasks such as inpainting and label-guided generation. The efficacy of ourapproach is due in part to the equivariance of our proposed framework underisometries, allowing our models to seamlessly reproduce details across locallysimilar regions and opening the door to a notion of generative texturetransfer.

Single Mesh Diffusion Models with Field Latents for Texture Generation