ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction
Abstract
Existing 4D Gaussian Splatting methods rely on per-Gaussian deformation from a canonical space to target frames, which overlooks redundancy among adjacent Gaussian primitives and result in suboptimal performance. To address this limitation, we propose Anchor-Driven Deformable and Compressed Gaussian Splatting (ADC-GS), a compact and efficient representation for dynamic scene reconstruction. Specifically, ADC-GS organizes Gaussian primitives into an anchor-based structure within the canonical space, enhanced by a temporal significance-based anchor refinement strategy. To reduce deformation redundancy, ADC-GS introduces a hierarchical coarse-to-fine pipeline that captures motions at varying granularities. Moreover, a rate-distortion optimization is adopted to achieve an optimal balance between bitrate consumption and representation fidelity. Experimental results demonstrate that ADC-GS outperforms the per-Gaussian deformation approaches in rendering speed by 300%-800% while achieving state-of-the-art storage efficiency without compromising rendering quality. The code is released at https://github.com/H-Huang774/ADC-GS.git.
Cite
Text
Huang et al. "ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/132Markdown
[Huang et al. "ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/huang2025ijcai-adc/) doi:10.24963/IJCAI.2025/132BibTeX
@inproceedings{huang2025ijcai-adc,
title = {{ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction}},
author = {Huang, He and Yang, Qi and Liu, Mufan and Xu, Yiling and Li, Zhu},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {1179-1187},
doi = {10.24963/IJCAI.2025/132},
url = {https://mlanthology.org/ijcai/2025/huang2025ijcai-adc/}
}