Fast3Dcache: Training-free 3D Geometry Synthesis Acceleration
Diffusion models have achieved impressive generative quality across modalities like 2D images, videos, and 3D shapes, but their inference remains computationally expensive due to the iterative denoising process. While recent caching-based methods effectively reuse redundant computations to speed up 2D and video generation, directly applying these techniques to 3D diffusion models can severely disrupt geometric consistency. In 3D synthesis, even minor numerical errors in cached latent features accumulate, causing structural artifacts and topological inconsistencies. To overcome this limitation, we propose Fast3Dcache, a training-free geometry-aware caching framework that accelerates 3D diffusion inference while preserving geometric fidelity. Our method introduces a Predictive Caching Scheduler Constraint (PCSC) to dynamically determine cache quotas according to voxel stabilization patterns and a Spatiotemporal Stability Criterion (SSC) to select stable features for reuse based on velocity magnitude and acceleration criterion. Comprehensive experiments show that Fast3Dcache accelerates inference significantly, achieving up to a 27.12% speed-up and a 54.8% reduction in FLOPs, with minimal degradation in geometric quality as measured by Chamfer Distance (2.48%) and F-Score (1.95%).
692d4dfb0ce2ec9461c8802f
Mengyu Yang
692d4dfb0ce2ec9461c88030
Yanming Yang
692d4dfb0ce2ec9461c88031
Chenyi Xu
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Chenxi Song
692d4dfb0ce2ec9461c88033
Yufan Zuo
692d4dfb0ce2ec9461c88034
Tong Zhao
692d4dfb0ce2ec9461c88035
Ruibo Li
692d4dfb0ce2ec9461c88036
Chi Zhang
Fast3Dcache: Training-free 3D Geometry Synthesis Acceleration
Diffusion models have achieved impressive generative quality across modalities like 2D images, videos, and 3D shapes, but their inference remains computationally expensive due to the iterative denoising process. While recent caching-based methods effectively reuse redundant computations to speed up 2D and video generation, directly applying these techniques to 3D diffusion models can severely disrupt geometric consistency. In 3D synthesis, even minor numerical errors in cached latent features accumulate, causing structural artifacts and topological inconsistencies. To overcome this limitation, we propose Fast3Dcache, a training-free geometry-aware caching framework that accelerates 3D diffusion inference while preserving geometric fidelity. Our method introduces a Predictive Caching Scheduler Constraint (PCSC) to dynamically determine cache quotas according to voxel stabilization patterns and a Spatiotemporal Stability Criterion (SSC) to select stable features for reuse based on velocity magnitude and acceleration criterion. Comprehensive experiments show that Fast3Dcache accelerates inference significantly, achieving up to a 27.12% speed-up and a 54.8% reduction in FLOPs, with minimal degradation in geometric quality as measured by Chamfer Distance (2.48%) and F-Score (1.95%).
Fast3Dcache accelerates 3D diffusion model inference with minimal geometric quality degradation by using a geometry-aware caching framework with dynamic cache quotas and spatiotemporal stability criteria.
diffusion models generative quality iterative denoising process caching-based methods geometric consistency voxel stabilization patterns Predictive Caching Scheduler Constraint Spatiotemporal Stability Criterion Chamfer Distance F-Score