VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion
  Models
Junlin Han Filippos Kokkinos Philip Torr
This paper presents a novel paradigm for building scalable 3D generative
models utilizing pre-trained video diffusion models. The primary obstacle in
developing foundation 3D generative models is the limited availability of 3D
data. Unlike images, texts, or videos, 3D data are not readily accessible and
are difficult to acquire. This results in a significant disparity in scale
compared to the vast quantities of other types of data. To address this issue,
we propose using a video diffusion model, trained with extensive volumes of
text, images, and videos, as a knowledge source for 3D data. By unlocking its
multi-view generative capabilities through fine-tuning, we generate a
large-scale synthetic multi-view dataset to train a feed-forward 3D generative
model. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view
data, can generate a 3D asset from a single image in seconds and achieves
superior performance when compared to current SOTA feed-forward 3D generative
models, with users preferring our results over 70% of the time.