AUTO-REGRESSIVE AUTO-ENCODER FOR ARTISTIC MESH GENERATION

Abstract

Automatic 3D content generation, particularly the generation of polygonal meshes, is useful for development of digital gaming, virtual reality, and filmmaking. Generative models in particular make 3D asset creation more accessible to non-experts. Some existing approaches rely on continuous 3D representations which lose the discrete face indices in triangular meshes during conversion and consequently require post-processing to extract triangular meshes which will then differ significantly from artist-created ones. More recently, attempts have been made to tokenize meshes into 1D sequences and leverage auto-regressive models for direct mesh generation, which can preserve the topology information and generate artistic meshes, but these methods are inefficient, result in accuracy loss, and cannot generalize beyond the training domain. The present disclosure provides an auto-regressive auto-encoder configured for artistic mesh generation, which can compress variable-length triangular meshes into fixed-length latent codes to enable training latent diffusion models conditioned on different modalities for improved generalization.

CPC Classifications