DRAWER: Digital Reconstruction and Articulation with Environment Realism

Abstract

Creating virtual digital replicas from real-world data unlocks significant potential across domains like gaming and robotics. In this paper, we present DRAWER, a novel framework that converts a video of a static indoor scene into a photorealistic and interactive digital environment. Our approach centers on two main contributions: (i) a reconstruction module based on a dual scene representation that reconstructs the scene with fine-grained geometric details, and (ii) an articulation module that identifies articulation types and hinge positions, reconstructs simulatable shapes and appearances and integrates them into the scene. The resulting virtual environment is photorealistic, interactive, and runs in real time, with compatibility for game engines and robotic simulation platforms. We demonstrate the potential of DRAWER by using it to automatically create an interactive game in Unreal Engine and to enable real-to-sim-to-real transfer for robotics applications. Project page: https://xiahongchi.github.io/DRAWER/.

Cite

Text

Xia et al. "DRAWER: Digital Reconstruction and Articulation with Environment Realism." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.02028

Markdown

[Xia et al. "DRAWER: Digital Reconstruction and Articulation with Environment Realism." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/xia2025cvpr-drawer/) doi:10.1109/CVPR52734.2025.02028

BibTeX

@inproceedings{xia2025cvpr-drawer,
  title     = {{DRAWER: Digital Reconstruction and Articulation with Environment Realism}},
  author    = {Xia, Hongchi and Su, Entong and Memmel, Marius and Jain, Arhan and Yu, Raymond and Mbiziwo-Tiapo, Numfor and Farhadi, Ali and Gupta, Abhishek and Wang, Shenlong and Ma, Wei-Chiu},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2025},
  pages     = {21771-21782},
  doi       = {10.1109/CVPR52734.2025.02028},
  url       = {https://mlanthology.org/cvpr/2025/xia2025cvpr-drawer/}
}