V3D：视频扩散模型是有效的3D生成器
V3D: Video Diffusion Models are Effective 3D Generators
AI总结：
本文提出V3D，通过几何一致性先验将预训练视频扩散模型扩展为多视图一致的3D生成器，可在3分钟内由单图生成高质量3D资产，并支持稀疏视图场景合成。
摘要：
自动化3D生成最近引起了广泛关注。近期方法大幅提升了生成速度，但由于模型容量或3D数据有限，通常只能生成细节较少的物体。受视频扩散模型最新进展的启发，我们提出了V3D，它利用预训练视频扩散模型的世界模拟能力来促进3D生成。为充分释放视频扩散感知3D世界的潜力，我们进一步引入几何一致性先验，并将视频扩散模型扩展为多视图一致的3D生成器。得益于此，只需给定单张图像，最先进的视频扩散模型即可经过微调，生成围绕物体的360度环绕视频帧。借助我们定制的重建流程，能够在3分钟内生成高质量网格或3D Gaussians。此外，该方法还可扩展到场景级新视角合成，在稀疏输入视图下实现对相机路径的精确控制。大量实验证明了所提方法的优越性能，尤其是在生成质量和多视图一致性方面。代码已发布于https://github.com/heheyas/V3D。
英文摘要：
Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency. Our code is available at https://github.com/heheyas/V3D
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
