Efficient 4D Gaussian Stream with Low Rank Adaptation

• URL: http://arxiv.org/abs/2502.16575v1
• Date: Sun, 23 Feb 2025 13:48:52 GMT
• Title: Efficient 4D Gaussian Stream with Low Rank Adaptation
• Authors: Zhenhuan Liu, Shuai Liu, Yidong Lu, Yirui Chen, Jie Yang, Wei Liu,
• Abstract summary: We propose a highly scalable method for dynamic novel view synthesis with continual learning.<n>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.
• Score: 8.69899446610606
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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