English

AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

Computer Vision and Pattern Recognition 2026-07-20 v1

Abstract

Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive. We present AniGS, a method for scene-level animation of 3D Gaussian Splatting (3DGS) reconstructions that adds subtle, distributed dynamics, e.g., vegetation motion, while preserving rigid structures. Unlike existing 3D animation techniques which are limited to object-centric subjects or small regions, AniGS is designed for large, cluttered, navigable scenes. AniGS represents the scene with a canonical 3DGS and models motion using a time-conditioned deformation field. To animate the entire scene, we leverage a pretrained video diffusion model and introduce an iterative dataset--model update strategy that progressively expands viewpoint coverage and repeatedly updates camera-fixed training videos using a render-and-refine scheme. To prevent artifacts from unintended motion in static areas, we further introduce a composed video-to-video refinement scheme that restricts motion to desired regions. Experiments on five real-world, large-scale outdoor scenes demonstrate that AniGS produces natural ambient dynamics and high-quality novel view videos, enabling more immersive viewing experiences of reconstructed environments.

Cite

@article{arxiv.2607.18539,
  title  = {AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation},
  author = {Yen-Chi Cheng and Chen Gao and Chuhan Chen and Tuotuo Li and Rajvi Shah and Ayush Saraf and Changil Kim and Liangyan Gui and Alexander Schwing and Johannes Kopf and Hung-Yu Tseng},
  journal= {arXiv preprint arXiv:2607.18539},
  year   = {2026}
}

Comments

Preprint. Project page: https://yccyenchicheng.github.io/AniGS/