Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape

arXiv:2305.15399v2 Announce Type: replace
Abstract: Synthesizing novel 3D models that resemble the input example has long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a diffusion model that learns the internal patch distribution from a single 3D textured shape and generates high-quality variations with fine geometry and texture details. Training a diffusion model directly in 3D would induce large memory and computational cost. Therefore, we first compress the input into a lower-dimensional latent …

3d models abstract artists arxiv cs.ai cs.cv cs.gr diffusion diffusion model distribution example graphics machine machine learning novel paper quality researchers resemble type