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arXiv · 2502.16575

Efficient 4D Gaussian Stream with Low Rank Adaptation

Zhenhuan Liu·Shuai Liu·Yidong Lu·Yirui Chen·Jie Yang·Wei Liu
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Abstract

Recent methods have made significant progress in synthesizing novel views with long video sequences. This paper proposes a highly scalable method for dynamic novel view synthesis with continual learning. We leverage the 3D Gaussians to represent the scene and a low-rank adaptation-based deformation model to capture the dynamic scene changes. Our method continuously reconstructs the dynamics with chunks of video frames, reduces the streaming bandwidth by $90\%$ while maintaining high rendering quality comparable to the off-line SOTA methods.

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Zhenhuan Liu, Shuai Liu, Yidong Lu, Yirui Chen, Jie Yang, Wei Liu. 2025-02-23. Efficient 4D Gaussian Stream with Low Rank Adaptation. https://arxiv.org/abs/2502.16575

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