Synthesizing Consistent Novel Views via 3D Epipolar Attention without Re-Training (2502.18219v1)

Abstract: Large diffusion models demonstrate remarkable zero-shot capabilities in novel view synthesis from a single image. However, these models often face challenges in maintaining consistency across novel and reference views. A crucial factor leading to this issue is the limited utilization of contextual information from reference views. Specifically, when there is an overlap in the viewing frustum between two views, it is essential to ensure that the corresponding regions maintain consistency in both geometry and appearance. This observation leads to a simple yet effective approach, where we propose to use epipolar geometry to locate and retrieve overlapping information from the input view. This information is then incorporated into the generation of target views, eliminating the need for training or fine-tuning, as the process requires no learnable parameters. Furthermore, to enhance the overall consistency of generated views, we extend the utilization of epipolar attention to a multi-view setting, allowing retrieval of overlapping information from the input view and other target views. Qualitative and quantitative experimental results demonstrate the effectiveness of our method in significantly improving the consistency of synthesized views without the need for any fine-tuning. Moreover, This enhancement also boosts the performance of downstream applications such as 3D reconstruction. The code is available at https://github.com/botaoye/ConsisSyn.

An Expert Review of "Synthesizing Consistent Novel Views via 3D Epipolar Attention Without Re-Training"

This paper delivers a comprehensive paper on how to improve view synthesis consistency in diffusion models without necessitating re-training, utilizing a novel approach based on 3D epipolar attention. The technique centers on leveraging epipolar geometry to optimize the utilization of contextual information from reference views, ensuring that regions with overlapping viewing frustums maintain geometric and visual consistency. This method requires no learnable parameters and eliminates the need for model fine-tuning, addressing a significant obstacle in the domain of single-image novel view synthesis.

The authors highlight the key challenges associated with employing large diffusion models for novel view synthesis, particularly focusing on ensuring consistency across synthesized views. The probabilistic nature of these models often results in inconsistencies, specifically when generating multi-view images. To combat these issues, the researchers propose epipolar attention mechanisms. By using epipolar lines, the method locates and retrieves overlapping information from an input view, integrating it seamlessly into the generation of target views. This process is elegantly structured to function without training, relying on explicit geometric constraints to guide image synthesis.

The paper presents both qualitative and quantitative experimental results which demonstrate a marked improvement in the consistency of synthesized views compared to previous methods, yielding stronger downstream applications such as 3D reconstruction. A significant claim by the authors is that their method significantly enhances performance metrics such as PSNR, SSIM, and LPIPS without necessitating additional model training. Interestingly, the paper also shows that improving multi-view image consistency enhances 3D reconstruction outcomes, further validating the utility of the proposed approach.

Epipolar attention, as introduced here, aligns with an optimization approach that does not rely on obtaining precise depth information, known to be a challenge in many real-world scenarios. This is a pivotal innovation, as it allows the system to deduce correspondence using 3D geometry priors alone, maintaining robustness in scenarios with complex occlusions or illumination changes.

The implications of this research extend to several areas in AI and computer vision, particularly in applications that require high consistency across generated views from single images, like virtual environment rendering, AR/VR content creation, and robotics. This method's ability to perform without retraining means it can potentially be applied universally across different diffusion models with minimal integration efforts.

Future developments could consider extending this approach to more complex scene types or employing it seamlessly with other consistency-driven synthesis tasks. Furthermore, evaluating the applicability of such a method in real-time environments or expanding it for use in interactive mediums might yield intriguing results.

Overall, the non-training 3D epipolar attention mechanism represents a noteworthy advancement in the synthesis of consistent novel views, directly addressing persistent challenges within the field and implying promising applications both practical and theoretical in nature.