ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction

TL;DR

ClipGStream introduces a hybrid framework for long, high-quality dynamic scene reconstruction that balances temporal stability and memory efficiency by modeling at the clip level.

Contribution

It proposes a novel clip-based stream optimization method with residual anchor compensation for scalable, flicker-free, and coherent dynamic scene reconstruction.

Findings

Achieves state-of-the-art quality in long dynamic scene reconstruction.

Reduces memory overhead compared to existing methods.

Maintains high temporal coherence across long sequences.

Abstract

Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with large-scale motion. Existing dynamic Gaussian approaches are either Frame-Stream, offering scalability but poor temporal stability, or Clip, achieving local consistency at the cost of high memory and limited sequence length. We propose ClipGStream, a hybrid reconstruction framework that performs stream optimization at the clip level rather than the frame level. The sequence is divided into short clips, where dynamic motion is modeled using clip-independent spatio-temporal fields and residual anchor compensation to capture local variations efficiently, while inter-clip inherited anchors and decoders maintain structural consistency across clips. This Clip-Stream design enables scalable, flicker-free reconstruction of long dynamic videos with high temporal coherence and reduced memory overhead. Extensive experiments demonstrate that ClipGStream achieves state-of-the-art reconstruction quality and efficiency. The project page is available at: https://liangjie1999.github.io/ClipGStreamWeb/