Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation
Journal:
arXiv
Published Date:
Mar 20, 2025
Abstract
We present Acc3D to tackle the challenge of accelerating the diffusion
process to generate 3D models from single images. To derive high-quality
reconstructions through few-step inferences, we emphasize the critical issue of
regularizing the learning of score function in states of random noise. To this
end, we propose edge consistency, i.e., consistent predictions across the high
signal-to-noise ratio region, to enhance a pre-trained diffusion model,
enabling a distillation-based refinement of the endpoint score function.
Building on those distilled diffusion models, we propose an adversarial
augmentation strategy to further enrich the generation detail and boost overall
generation quality. The two modules complement each other, mutually reinforcing
to elevate generative performance. Extensive experiments demonstrate that our
Acc3D not only achieves over a $20\times$ increase in computational efficiency
but also yields notable quality improvements, compared to the
state-of-the-arts.
