PhysGen3D: Crafting a Miniature Interactive World from a Single Image

Abstract

Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce MiniTwin, a novel framework that transforms a single image into an amodal, camera-centric, interactive 3D scene. By combining advanced image-based geometric and semantic understanding with physics-based simulation, MiniTwin creates an interactive 3D world from a static image, enabling us to "imagine" and simulate future scenarios based on user input. At its core, MiniTwin estimates 3D shapes, poses, physical and lighting properties of objects, thereby capturing essential physical attributes that drive realistic object interactions. This framework allows users to specify precise initial conditions, such as object speed or material properties, for enhanced control over generated video outcomes. We evaluate MiniTwin's performance against closed-source state-of-the-art (SOTA) image-to-video models, including Pika, Kling, and Gen-3, showing MiniTwin's capacity to generate videos with realistic physics while offering greater flexibility and fine-grained control. Our results show that MiniTwin achieves a unique balance of photorealism, physical plausibility, and user-driven interactivity, opening new possibilities for generating dynamic, physics-grounded video from an image.

Cite

Text

Chen et al. "PhysGen3D: Crafting a Miniature Interactive World from a Single Image." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.00579

Markdown

[Chen et al. "PhysGen3D: Crafting a Miniature Interactive World from a Single Image." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/chen2025cvpr-physgen3d/) doi:10.1109/CVPR52734.2025.00579

BibTeX

@inproceedings{chen2025cvpr-physgen3d,
  title     = {{PhysGen3D: Crafting a Miniature Interactive World from a Single Image}},
  author    = {Chen, Boyuan and Jiang, Hanxiao and Liu, Shaowei and Gupta, Saurabh and Li, Yunzhu and Zhao, Hao and Wang, Shenlong},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2025},
  pages     = {6178-6189},
  doi       = {10.1109/CVPR52734.2025.00579},
  url       = {https://mlanthology.org/cvpr/2025/chen2025cvpr-physgen3d/}
}