S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points

Recently, the dynamic scene reconstruction using Gaussians has garnered increased interest. Mainstream approaches typically employ a global deformation field to warp a 3D scene in the canonical space. However, the inherently low-frequency nature of implicit neural fields often leads to ineffective representations of complex motions. Moreover, their structural rigidity can hinder adaptation to scenes with varying resolutions and durations. To overcome these challenges, we introduce a novel approach utilizing discrete 3D control points. This method models local rays physically and establishes a motion-decoupling coordinate system, which effectively merges traditional graphics with learnable pipelines for a robust and efficient local 6-degrees-of-freedom (6-DoF) motion representation. Additionally, we have developed a generalized framework that incorporates our control points with Gaussians. Starting from an initial 3D reconstruction, our workflow decomposes the streaming 4D real-world reconstruction into four independent submodules: 3D segmentation, 3D control points generation, object-wise motion manipulation, and residual compensation. Our experiments demonstrate that this method outperforms existing state-of-the-art 4D Gaussian Splatting techniques on both the Neu3DV and CMU-Panoptic datasets. Our approach also significantly accelerates training, with the optimization of our 3D control points achievable within just 2 seconds per frame on a single NVIDIA 4070 GPU.