SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints (2412.07760v1)

Abstract: Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency. This progress inspires us to investigate the potential of these models to ensure dynamic consistency across various viewpoints, a highly desirable feature for applications such as virtual filming. Unlike existing methods focused on multi-view generation of single objects for 4D reconstruction, our interest lies in generating open-world videos from arbitrary viewpoints, incorporating 6 DoF camera poses. To achieve this, we propose a plug-and-play module that enhances a pre-trained text-to-video model for multi-camera video generation, ensuring consistent content across different viewpoints. Specifically, we introduce a multi-view synchronization module to maintain appearance and geometry consistency across these viewpoints. Given the scarcity of high-quality training data, we design a hybrid training scheme that leverages multi-camera images and monocular videos to supplement Unreal Engine-rendered multi-camera videos. Furthermore, our method enables intriguing extensions, such as re-rendering a video from novel viewpoints. We also release a multi-view synchronized video dataset, named SynCamVideo-Dataset. Project page: https://jianhongbai.github.io/SynCamMaster/.

• The paper introduces a multi-view synchronization module that enhances geometric and visual coherence across diverse camera perspectives.
• The methodology integrates a hybrid training scheme combining multi-camera images, monocular videos, and synthetic Unreal Engine data to boost performance.
• Experimental results show improved synchronization accuracy and visual fidelity, outperforming baselines with superior FVD and CLIP scores.

Overview of SynCamMaster: Synchronizing Multi-Camera Video Generation

This paper presents SynCamMaster, a new approach to generating synchronized videos from multiple cameras, aimed at supporting applications such as virtual filming. The key innovation of SynCamMaster is its ability to generate open-domain videos from diverse viewpoints while maintaining dynamic and geometric consistency. This capability is achieved through the integration of a multi-view synchronization module in a pre-trained text-to-video (T2V) diffusion model. This approach focuses on improving the alignment and consistency of videos captured simultaneously from different camera angles and positions.

Key Contributions

• Multi-view Synchronization: A novel multi-view synchronization module is designed to maintain consistency across viewpoints. This module performs cross-view attention to modulate geometric and visual coherence, which is essential for generating consistent scenes from various camera perspectives.
• Hybrid Training Scheme: Due to the lack of comprehensive multi-view video datasets, the authors propose a hybrid training approach. This scheme leverages a combination of multi-camera images, monocular videos, and synthetic data generated from Unreal Engine. This diverse data collection enhances the model's ability to generalize across different scenes and viewing angles.
• Plug-and-play Module: SynCamMaster introduces a plug-and-play module that can be easily added to existing T2V models to enable synchronized multi-view video generation. This approach makes it flexible and adaptable for integration with other diffusion models.
• Novel View Video Synthesis Extension: The method is extendable to re-render videos from new viewpoints, showcasing the model's versatility in various video generation tasks.

Experimental Results

The authors conduct extensive experiments to demonstrate the effectiveness of SynCamMaster. The model shows significant improvements over baseline methods in terms of synchronization accuracy and visual fidelity. Specifically, metrics such as Frechet Video Distance (FVD) and CLIP similarity scores indicate superior performance. Notably, the method achieves better synchronization across complex scenes and varying viewpoints.

Implications and Future Work

The practical implications of this research are substantial in the context of virtual filming, animation, and augmented reality. By enabling consistent video generation from multiple cameras, SynCamMaster provides filmmakers and content creators with tools to explore new storytelling possibilities with coherent scene captures from various perspectives.

On a theoretical level, the proposed framework could inspire further exploration into synchronization modules and hybrid training techniques. Future work could focus on expanding the dataset diversity, improving scene complexity handling, and refining synchronization for more intricate movements and larger variations in camera positions.

Conclusion

SynCamMaster represents a significant step forward in multi-camera video generation, addressing a challenging gap in 3D consistency and synchronization across diverse viewpoints. By leveraging a hybrid-data approach and innovative synchronization techniques, it provides a promising pathway for advancements in video generation and virtual content creation. This research paves the way for future investigations into AI-driven video synthesis, with potential applications extending beyond entertainment to include areas like education, remote collaboration, and immersive experiences.