GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation (2403.12365v2)

Abstract: Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored. In this paper, we introduce a novel concept, Gaussian flow, which connects the dynamics of 3D Gaussians and pixel velocities between consecutive frames. The Gaussian flow can be efficiently obtained by splatting Gaussian dynamics into the image space. This differentiable process enables direct dynamic supervision from optical flow. Our method significantly benefits 4D dynamic content generation and 4D novel view synthesis with Gaussian Splatting, especially for contents with rich motions that are hard to be handled by existing methods. The common color drifting issue that happens in 4D generation is also resolved with improved Guassian dynamics. Superior visual quality on extensive experiments demonstrates our method's effectiveness. Quantitative and qualitative evaluations show that our method achieves state-of-the-art results on both tasks of 4D generation and 4D novel view synthesis. Project page: https://zerg-overmind.github.io/GaussianFlow.github.io/

• The paper introduces GaussianFlow to bridge 3D Gaussian dynamics with 2D optical flows, enhancing 4D dynamic scene synthesis.
• It employs a fully differentiable projection of 3D Gaussians into 2D image space, effectively mitigating color drifting and managing fast motions.
• The methodology demonstrates strong potential for real-time rendering and improved novel view synthesis in dynamic, real-world applications.

GaussianFlow: Bridging 3D Gaussian Dynamics with 2D Pixel Velocities for Enhanced 4D Content Creation

Introduction to 4D Dynamic Content Creation

The creation of 4D dynamic content from videos has tangible applications in various domains, including virtual and augmented reality, gaming, and the film industry. Recent approaches in this space have chiefly focused on utilizing Neural Radiance Fields (NeRF) and Gaussian Splatting techniques to model 4D scenes. However, optimizing these models, especially for content rich in motion, poses significant challenges due to the under-constrained nature of motion dynamics when derived from monocular videos or synthetic text-to-video outputs.

Gaussian Flow: Concept and Implementation

In response to the outlined challenges, this paper introduces the concept of Gaussian Flow, a technique that effectively connects the dynamics of 3D Gaussians with 2D pixel velocities between consecutive frames. This connection allows for the direct supervision of 3D Gaussian dynamics using optical flow data, substantially benefiting tasks such as 4D content generation and 4D novel view synthesis.

The computation of Gaussian Flow is based on the projection of 3D Gaussian dynamics into 2D image spaces, a process which is both efficient and fully differentiable. This enables improved handling of scene sequences characterized by fast motions and provides a solution to common color drifting issues observed in 4D content generation. The foundational contributions of this work can be summarized as follows:

• Introduction of the Gaussian Flow concept, bridging 3D Gaussian dynamics with 2D pixel velocities.
• Efficient and differentiable computation of Gaussian Flow by projecting Gaussian dynamics into image space.
• Significant enhancements in 4D content generation and 4D novel view synthesis, especially in handling rich motion sequences and mitigating color drifting issues.

Practical and Theoretical Implications

This research substantially advances the state-of-the-art in 4D content creation, offering theoretical contributions that elucidate the relationship between 3D motions and their 2D projections. Practically, the framework demonstrates superior visual quality across various tests and highlights potential for real-time rendering applications without necessitating specialized designs.

Speculating on Future Developments

The introduction of Gaussian Flow opens avenues for further exploration in the field of 4D content creation. Future research could delve into long-term flow supervision across multiple frames for even smoother dynamics. Additionally, investigating the application of view-conditioned flow supervisions in generative tasks might yield advancements in achieving temporal consistency across novel views, thereby further reducing the gap between synthetic and real-world applicability.

Conclusion

Gaussian Flow represents a significant methodological advancement in the modeling of 4D dynamic scenes. By providing a mechanism for the direct supervision of 3D Gaussian dynamics through 2D optical flows, this approach not only addresses critical challenges in 4D content creation but also lays the groundwork for exciting future research in this rapidly evolving field.