Novel View Extrapolation with Video Diffusion Priors (2411.14208v1)

Abstract: The field of novel view synthesis has made significant strides thanks to the development of radiance field methods. However, most radiance field techniques are far better at novel view interpolation than novel view extrapolation where the synthesis novel views are far beyond the observed training views. We design ViewExtrapolator, a novel view synthesis approach that leverages the generative priors of Stable Video Diffusion (SVD) for realistic novel view extrapolation. By redesigning the SVD denoising process, ViewExtrapolator refines the artifact-prone views rendered by radiance fields, greatly enhancing the clarity and realism of the synthesized novel views. ViewExtrapolator is a generic novel view extrapolator that can work with different types of 3D rendering such as views rendered from point clouds when only a single view or monocular video is available. Additionally, ViewExtrapolator requires no fine-tuning of SVD, making it both data-efficient and computation-efficient. Extensive experiments demonstrate the superiority of ViewExtrapolator in novel view extrapolation. Project page: \url{https://kunhao-liu.github.io/ViewExtrapolator/}.

• The paper presents ViewExtrapolator, which integrates generative video diffusion priors to refine and enhance the synthesis of extrapolated views.
• It introduces innovative techniques like guidance and resampling annealing to mitigate artifacts and improve photorealism in 3D rendering.
• Extensive experiments demonstrate superior SSIM, PSNR, and LPIPS metrics, highlighting the method's potential for immersive 3D visual applications.

Overview of "Novel View Extrapolation with Video Diffusion Priors"

The paper "Novel View Extrapolation with Video Diffusion Priors" introduces ViewExtrapolator, a method designed to tackle the limitations of existing radiance field methods in the context of novel view extrapolation. While traditional radiance field techniques such as NeRF, Instant-NGP, and 3D Gaussian Splatting excel in novel view interpolation, their effectiveness diminishes significantly when the target views lie beyond the convex hull of the observed training views. This work addresses this gap by leveraging the generative capabilities of Stable Video Diffusion (SVD) models to enhance the realism and clarity of extrapolated views.

Methodological Contributions

ViewExtrapolator is centered around the integration of generative diffusion priors into the view synthesis process, specifically targeting the challenging domain of novel view extrapolation. The approach involves redesigning the denoising process of the SVD model to guide it towards refining the artifact-laden outputs of radiance fields, enabling the synthesis of more photorealistic novel views. The authors introduce several innovative mechanisms in this context:

1. Guidance Annealing and Resampling Annealing: These techniques are instrumental in mitigating the influence of artifacts in the denoising process. Guidance annealing reduces the strength of guidance during the denoising stages where finer details are incrementally added, while resampling annealing ensures consistent quality improvement throughout the refinement process by allowing multiple denoising attempts.
2. Training-Free Adaptability: ViewExtrapolator operates at the inference stage without the need for fine-tuning, making it both computationally and data-efficient. It is versatile enough to enhance view sequences from different types of 3D renderings, including those derived from monocular videos or single views using point clouds.

Experimental Evaluation

The authors conducted extensive experiments to evaluate the performance of ViewExtrapolator across various settings. Quantitative results demonstrate that the method achieves superior metrics (SSIM, PSNR, and LPIPS) compared to baseline approaches like 3D Gaussian Splatting and its depth-regularized variant. The qualitative assessments further corroborate these findings, showcasing significant reduction in artifacts and improved rendering quality for extrapolated novel views. The methodology effectively addresses the key challenge of generating unseen content while maintaining multi-view consistency and detail accuracy.

Implications and Future Directions

The implications of this work are significant in the advancement of immersive 3D experiences, where users can freely navigate reconstructed radiance fields without being confined to interpolated views. The introduction of generative priors from large-scale video diffusion models provides a promising pathway for overcoming the current limitations of novel view synthesis techniques.

Looking forward, this approach could spur further research into integrating more sophisticated generative models and exploring the potential of personalized and dynamic scene extrapolations. Additionally, its training-free nature hints at widespread applicability across various domains, from virtual reality and gaming to advanced content creation in film and animation.

In conclusion, the paper presents a notable step forward in novel view synthesis by reframing the challenge of view extrapolation through the lens of generative video models, emphasizing adaptability, efficiency, and the pursuit of photorealistic detail.