  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
