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FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction

About

This paper addresses the challenge of reconstructing dynamic 3D scenes with complex motions. Some recent works define 3D Gaussian primitives in the canonical space and use deformation fields to map canonical primitives to observation spaces, achieving real-time dynamic view synthesis. However, these methods often struggle to handle scenes with complex motions due to the difficulty of optimizing deformation fields. To overcome this problem, we propose FreeTimeGS, a novel 4D representation that allows Gaussian primitives to appear at arbitrary time and locations. In contrast to canonical Gaussian primitives, our representation possesses the strong flexibility, thus improving the ability to model dynamic 3D scenes. In addition, we endow each Gaussian primitive with an motion function, allowing it to move to neighboring regions over time, which reduces the temporal redundancy. Experiments results on several datasets show that the rendering quality of our method outperforms recent methods by a large margin. Project page: https://zju3dv.github.io/freetimegs/ .

Yifan Wang, Peishan Yang, Zhen Xu, Jiaming Sun, Zhanhua Zhang, Yong Chen, Hujun Bao, Sida Peng, Xiaowei Zhou• 2025

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisNeu3D (test)
PSNR33.19
18
Dynamic Scene ReconstructionNeural 3D Video 19 (full)
PSNR33.19
17
Dynamic View SynthesisNeural 3D Video 19 (test)
PSNR33.19
16
Dynamic View SynthesisSelfCap ours (test)
PSNR29.38
6
Novel View SynthesisNeural3DV
PSNR33.19
6
Novel View SynthesisENeRF-Outdoor
PSNR25.36
6
Novel View SynthesisSelfCap
PSNR27.5
6
Dynamic Scene ReconstructionSelfCap entire image
PSNR27.41
5
Dynamic Scene ReconstructionSelfCap dynamic regions
PSNR29.38
5
Dynamic View SynthesisENeRF-Outdoor 23 (test)
PSNR25.36
5
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