Multi-Scale Latent Points Consistency Models for 3D Shape Generation
Keywords: Point Cloud Generation, diffusion model, consistency model
Abstract: Consistency Models (CM) 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 Points 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.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 2201
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