Multi-scale Latent Point Consistency Models for 3D Shape Generation
This addresses the problem of slow sampling in 3D shape generation for computer graphics and AI applications, representing a strong incremental improvement over existing methods.
The paper tackled 3D shape generation from point clouds by proposing a Multi-scale Latent Point Consistency Model, which achieved a 100x speedup in generation while surpassing state-of-the-art diffusion models in shape quality and diversity on ShapeNet benchmarks.
Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). Our MLPCM follows a latent diffusion framework and introduces hierarchical levels of latent representations, ranging from point-level to super-point levels, each corresponding to a different spatial resolution. We design a multi-scale latent integration module along with 3D spatial attention to effectively denoise the point-level latent representations conditioned on those from multiple super-point levels. Additionally, we propose a latent consistency model, learned through consistency distillation, that compresses the prior into a one-step generator. This significantly improves sampling efficiency while preserving the performance of the original teacher model. Extensive experiments on standard benchmarks ShapeNet and ShapeNet-Vol demonstrate that MLPCM achieves a 100x speedup in the generation process, while surpassing state-of-the-art diffusion models in terms of both shape quality and diversity.