SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes (2312.14937v3)

Abstract: Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point, which reduces learning complexities, enhances learning abilities, and facilitates obtaining temporal and spatial coherent motion patterns. Then, we jointly learn the 3D Gaussians, the canonical space locations of control points, and the deformation MLP to reconstruct the appearance, geometry, and dynamics of 3D scenes. During learning, the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions, and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally, thanks to the explicit sparse motion representation and its decomposition from appearance, our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications. Project page: https://yihua7.github.io/SC-GS-web/

• The paper introduces a novel approach that decomposes scene motion into sparse control points and dense Gaussians for efficient dynamic scene rendering.
• It employs a deformation MLP and ARAP loss to predict 6 DoF transformations, ensuring spatial and temporal coherence across views.
• Experiments show improved PSNR, SSIM, and LPIPS metrics, demonstrating its potential for real-time applications in virtual reality and gaming.

Analysis of "SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes"

The paper "SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes" introduces a novel approach to novel view synthesis in dynamic scenes, building upon the Gaussian splatting technique. This work presents a focused effort to overcome the challenges associated with rendering dynamic scenes, which have been traditionally difficult due to varying motion complexities and limited observational data.

Sparse Control Points and Gaussian Splatting

The core proposal of this research is the decomposition of scene motion and appearance into sparse control points and dense Gaussians. This separation allows for efficient high-fidelity rendering and enables intuitive motion editing. The sparse control points operate as compact motion bases in a high-dimensional space, facilitating the modeling of 6 DoF transformations via a deformation MLP (Multi-Layer Perceptron). This MLP predicts time-variant motions, significantly simplifying the learning complexity and enhancing the spatial and temporal coherence of the scene dynamics.

Dynamic Scene Rendering and Optimization

The paper outlines a rendering mechanism based on Gaussian splatting, where the 3D scene is represented by colored 3D Gaussians. This is achieved by projecting Gaussians onto a 2D image plane and aggregating them with a fast alpha-blending method. The scene’s dynamics are represented through adaptive control point transformations, utilizing a locally rigid motion hypothesis and regularization from an as-rigid-as-possible (ARAP) loss. This ensures local continuity and enhances the spatial fidelity of the resultant views.

The research incorporates an adaptive strategy for dynamically adjusting the density and location of control points during training. This is achieved through a pruning and cloning mechanism that optimizes control point distribution according to motion complexities.

Numerical Performance and Claims

The experiments demonstrate that the SC-GS method outperforms existing techniques on established benchmark datasets like D-NeRF and NeRF-DS. The paper reports superior quantitative results in terms of PSNR, SSIM, and LPIPS metrics, as well as significant improvements in rendering speed. This suggests an effective balance between rendering quality and efficiency, positioning it as a feasible solution for real-time applications in gaming and virtual reality.

Implications and Future Directions

The implications of this research are notable both in practice and theory. Practically, the development of a sparse motion representation enables user-controlled motion editing in dynamic scenes without sacrificing visual fidelity. Theoretically, the decomposition approach opens up new pathways in managing the complexity of scene dynamics through controlled Gaussian representations.

Future research may focus on addressing certain limitations, such as the sensitivity to camera pose inaccuracies and potential overfitting in sparse viewpoint scenarios. Additionally, extending the approach to handle more intense and rapid movements effectively would further broaden its applicability.

Conclusion

The SC-GS framework stands out for its innovative use of sparse control points to efficiently handle the complexities of dynamic scene rendering and editing. By enhancing both the quality and speed of novel view synthesis, this method lays a strong foundation for future explorations in dynamic scene representations. Such advancements align closely with the evolving demands for realistic graphics in interactive media and immersive environments.