ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models

TL;DR

This paper introduces a simple yet effective method for novel view synthesis from a single image by leveraging pre-trained video diffusion models to generate spatially consistent views along a camera trajectory.

Contribution

The work reformulates view synthesis as video generation using a pre-trained video diffusion model, achieving high consistency and quality in novel view rendering.

Findings

Outperforms state-of-the-art methods in view synthesis quality

Achieves high spatial consistency in generated views

Utilizes pre-trained video diffusion models for improved results

Abstract

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality, spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress, achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work, we demonstrate a strikingly simple method, where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a camera going around the object of interest -- a scanning video -- which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus, to perform novel-view synthesis, we create a smooth camera trajectory to the target view that we wish to render, and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so, we obtain a highly consistent novel view synthesis, outperforming the state of the art.