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

Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, Kwang Moo Yi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 6775-6785

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.