SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image
  using Latent Video Diffusion
Vikram Voleti Chun-Han Yao Mark Boss Adam Letts David Pankratz Dmitry Tochilkin Christian Laforte Robin Rombach Varun Jampani
We present Stable Video 3D (SV3D) -- a latent video diffusion model for
high-resolution, image-to-multi-view generation of orbital videos around a 3D
object. Recent work on 3D generation propose techniques to adapt 2D generative
models for novel view synthesis (NVS) and 3D optimization. However, these
methods have several disadvantages due to either limited views or inconsistent
NVS, thereby affecting the performance of 3D object generation. In this work,
we propose SV3D that adapts image-to-video diffusion model for novel multi-view
synthesis and 3D generation, thereby leveraging the generalization and
multi-view consistency of the video models, while further adding explicit
camera control for NVS. We also propose improved 3D optimization techniques to
use SV3D and its NVS outputs for image-to-3D generation. Extensive experimental
results on multiple datasets with 2D and 3D metrics as well as user study
demonstrate SV3D's state-of-the-art performance on NVS as well as 3D
reconstruction compared to prior works.