{"type":"ScholarlyArticle","title":"ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction","authors":[{"name":"He Huang","slug":"he-huang","given":"He","family":"Huang","affiliation":""},{"name":"Qi Yang","slug":"qi-yang","given":"Qi","family":"Yang","affiliation":""},{"name":"Mufan Liu","slug":"mufan-liu","given":"Mufan","family":"Liu","affiliation":""},{"name":"Yiling Xu","slug":"yiling-xu","given":"Yiling","family":"Xu","affiliation":""},{"name":"Zhu Li","slug":"zhu-li","given":"Zhu","family":"Li","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/132","url":"https://ijcai.org/proceedings/2025/132","pdf":"https://ijcai.org/proceedings/2025/0132.pdf","markdown":"https://ijcai.org/proceedings/2025/132.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/132","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":132,"pages":{"first":1179,"last":1187},"keywords":["Computer Vision: CV: Efficiency and Optimization","Computer Vision: CV: Image and video synthesis and generation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}