Generative Novel View Synthesis with 3D-Aware Diffusion Models (2304.02602v1)

Abstract: We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image. Our model samples from the distribution of possible renderings consistent with the input and, even in the presence of ambiguity, is capable of rendering diverse and plausible novel views. To achieve this, our method makes use of existing 2D diffusion backbones but, crucially, incorporates geometry priors in the form of a 3D feature volume. This latent feature field captures the distribution over possible scene representations and improves our method's ability to generate view-consistent novel renderings. In addition to generating novel views, our method has the ability to autoregressively synthesize 3D-consistent sequences. We demonstrate state-of-the-art results on synthetic renderings and room-scale scenes; we also show compelling results for challenging, real-world objects.

Generative Novel View Synthesis with 3D-Aware Diffusion Models

The paper "Generative Novel View Synthesis with 3D-Aware Diffusion Models" presents a framework that significantly contributes to the advancement of novel view synthesis (NVS) by leveraging a diffusion model conditioned on 3D neural features. This framework allows for synthesizing novel views from limited input data, achieving state-of-the-art quality, as evidenced by experimental results on synthetic and real-world datasets.

Methodological Innovation

The central innovation of the paper lies in its integration of 2D diffusion models with 3D geometry priors. The approach builds upon existing diffusion model architectures but modifies them to incorporate a 3D feature volume, which acts as a latent representation of the scene. This geometry-aware model enhances the consistency of renderings across views, even when input is sparse or ambiguous. By using a 3D neural feature field, the model captures a distribution of potential scene representations and synthesizes varied and plausible view-consistent outputs.

Two distinct capabilities of the model are highlighted:

1. Novel View Synthesis: The model can generate a realistic view from as little as one input image by sampling from a distribution of possible scenes.
2. Autoregressive Sequence Generation: It allows for creating 3D-consistent sequences of images, effectively rendering smooth and consistent transitions between views.

Experimental Results

In qualitative and quantitative evaluations, the method demonstrates superior performance on datasets like ShapeNet and Matterport3D. The results showcase the framework's ability to manage both synthetic and real-world scene complexities, including room-scale setups. Numerical evaluations, employing metrics such as FID, LPIPS, and Chamfer distance, confirm its competitive edge over existing regression-based and geometry-free generative methods.

On the ShapeNet dataset, the proposed method surpasses baselines by producing sharper and more detailed renderings. The method also proves effective in challenging settings like the CO3D dataset, demonstrating a strong capacity to handle ambiguous and complex real-world scenes.

Implications and Future Directions

This work opens several pathways for future developments in novel view synthesis:

• Scalability and Resolution: While current implementations are constrained to lower resolutions, leveraging advancements in diffusion models can potentially upscale the synthesized images.
• Speed Optimization: The diffusion-based approach, while powerful, could benefit from further optimization to meet real-time processing demands.
• Enhanced Consistency: Continued research might focus on improving the temporal and geometric consistency without sacrificing the flexibility or diversity of generated views.

Furthermore, integrating such generative models with application-specific constraints could widen their utility in areas such as virtual reality, augmented reality, and autonomous navigation.

Conclusion

This paper's contribution to the field of novel view synthesis is significant, providing a robust framework that marries diffusion models with 3D geometric representations. The model's capability to synthesize coherent 3D aware sequences from minimal input highlights its potential for real-world applications and sets a benchmark for future research in generative modeling and view synthesis.