3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance

TL;DR

This paper presents a novel method that combines 3D-free and 3D-based approaches to generate high-quality, camera-controlled novel views from a single image, effectively handling complex scenes without extensive training data.

Contribution

It introduces a new approach that leverages pretrained diffusion models and enriches CLIP with 3D camera info for versatile, high-fidelity view synthesis from one image.

Findings

Outperforms existing models in qualitative and quantitative evaluations.

Achieves high-fidelity, consistent novel views at specified camera angles.

Handles complex scenes without extensive training or additional 3D data.

Abstract

Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution. Moreover, they tend to be object centric and struggle with complex and intricate scenes. Conversely, 3D-free methods can generate text-controlled views of complex, in-the-wild scenes using a pretrained stable diffusion model without the need for a large amount of 3D-based training data, but lack camera control. In this paper, we introduce a method capable of generating camera-controlled viewpoints from a single input image, by combining the benefits of 3D-free and 3D-based approaches. Our method excels in handling complex and diverse scenes without extensive training or additional 3D and multiview data. It leverages widely available pretrained NVS models for weak guidance, integrating this knowledge into a 3D-free view synthesis style approach, along with enriching the CLIP vision-language space with 3D camera angle information, to achieve the desired results. Experimental results demonstrate that our method outperforms existing models in both qualitative and quantitative evaluations, achieving high-fidelity, consistent novel view synthesis at desired camera angles across a wide variety of scenes while maintaining accurate, natural detail representation and image clarity across various viewpoints. We also support our method with a comprehensive analysis of 2D image generation models and the 3D space, providing a solid foundation and rationale for our solution.