Hash3D: Training-free Acceleration for 3D Generation
Keywords: Efficient 3D generation; Score distillation
TL;DR: Hash3D accelerates 3D generation up to 4× by reusing redundant feature maps through adaptive grid-based hashing, enhancing speed and view consistency without additional model training.
Abstract: The quality of 3D generative modeling has been notably improved by the adoption of 2D diffusion models. Despite this progress,
the cumbersome optimization process \emph{per se}
presents a critical problem to efficiency.
In this paper,
we introduce Hash3D, a universal acceleration for 3D score distillation sampling~(SDS) without model training.
Central to Hash3D is the observation that images rendered from similar camera positions and diffusion time-steps often have redundant feature maps. By hashing and reusing these feature maps across nearby timesteps and camera angles, Hash3D eliminates unnecessary calculations. We implement this through an adaptive grid-based hashing. As a result, it largely speeds up the process of 3D generation.
Surprisingly, this feature-sharing mechanism not only makes generation faster but also improves the smoothness and view consistency of the synthesized 3D objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models, demonstrate Hash3D’s versatility to speed up optimization, enhancing efficiency by $1.5\sim 4\times$. Additionally, Hash3D's integration with 3D Gaussian splatting largely speeds up 3D model creation, reducing text-to-3D processing to about 10 minutes and image-to-3D conversion to roughly 30 seconds.
Primary Area: generative models
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Submission Number: 836
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