Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention (2410.10774v1)

Abstract: In recent years there have been remarkable breakthroughs in image-to-video generation. However, the 3D consistency and camera controllability of generated frames have remained unsolved. Recent studies have attempted to incorporate camera control into the generation process, but their results are often limited to simple trajectories or lack the ability to generate consistent videos from multiple distinct camera paths for the same scene. To address these limitations, we introduce Cavia, a novel framework for camera-controllable, multi-view video generation, capable of converting an input image into multiple spatiotemporally consistent videos. Our framework extends the spatial and temporal attention modules into view-integrated attention modules, improving both viewpoint and temporal consistency. This flexible design allows for joint training with diverse curated data sources, including scene-level static videos, object-level synthetic multi-view dynamic videos, and real-world monocular dynamic videos. To our best knowledge, Cavia is the first of its kind that allows the user to precisely specify camera motion while obtaining object motion. Extensive experiments demonstrate that Cavia surpasses state-of-the-art methods in terms of geometric consistency and perceptual quality. Project Page: https://ir1d.github.io/Cavia/

• The paper introduces a novel framework using view-integrated attention, achieving enhanced spatiotemporal consistency and precise camera control in multi-view video diffusion.
• It innovates with cross-frame and cross-view attention mechanisms, leveraging Plücker coordinates to encode camera information for robust 3D consistency.
• Experimental results demonstrate improved geometric precision and visual quality, establishing Cavia as a scalable solution for realistic video generation.

Overview of "Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention"

The paper introduces Cavia, a framework designed to address the challenges of generating camera-controllable multi-view videos from input images, focusing on achieving spatiotemporal consistency across different camera paths. It specifically targets the limitations of prior attempts in generating consistent 3D-rendered video sequences with flexible camera movements. Cavia enhances video diffusion models by integrating novel view-integrated attention mechanisms.

Methodology

The framework builds upon Stable Video Diffusion (SVD), a variant of Stable Diffusion enhanced with temporal layers for video generation. Cavia's primary innovation is the introduction of Plücker coordinates for encoding camera information, combined with cross-frame and cross-view attention mechanisms.

1. Cross-frame Attention:
   • Inflates traditional 1D temporal attention to 3D, allowing simultaneous modeling of spatial and temporal coherence. This enables enhanced handling of large pixel displacements during significant viewpoint changes.
2. Cross-view Attention:
   • Extends spatial attention to facilitate feature exchange across multiple viewpoints. This ensures consistency across the different generated views and supports generation from arbitrary camera trajectories.
3. Training Strategy:
   • Employs a joint training scheme utilizing static scene videos, synthetic object renderings, and annotated monocular videos. This diverse training set helps balance the model's ability to generate realistic object motions and maintain complex scene backgrounds.

Experimental Results

Cavia demonstrates superior performance compared to existing methods, such as MotionCtrl and CameraCtrl. The evaluations cover:

• Geometric Consistency: Using COLMAP-based metrics, Cavia achieves lower reconstruction errors, supporting its capability to maintain 3D consistency across frames.
• Visual Quality: Assessed by FID and FVD scores, Cavia results in better perceptual quality in both monocular and multi-view scenarios.
• Multi-view Consistency: Cavia excels in maintaining consistency across multiple video sequences, showing clear improvements in precision and matching scores.

Implications and Future Directions

The development of Cavia represents a significant advancement in the control and consistency of video generation models. The integration of cross-frame and cross-view attentions offers a scalable solution to achieve realistic video generation with camera flexibility.

Practical Implications

Cavia's capability to generate consistent multi-view videos holds potential for several applications, including virtual content creation, immersive media, and real-time scene reconstruction. It is particularly useful for scenarios requiring precise camera motion and dynamic object rendering.

Theoretical Contributions

By leveraging advanced attention mechanisms, Cavia contributes to the understanding of spatiotemporal coherence in generative models. It paves the way for future exploration of enhanced conditioning techniques in video diffusion processes and could inspire improvements in other generative model domains.

Future Work

The paper alludes to potential extensions, such as improving the handling of larger object motions and adapting to complex camera models commonly used in professional cinematography. It suggests exploring calibration techniques to accurately capture metric scales, fostering even more realistic video synthesis.

In summary, Cavia addresses significant challenges in multi-view video generation, offering a robust framework that advances the field in terms of control, consistency, and quality. Its design principles and results provide a compelling foundation for ongoing research and development in video diffusion models.