| license: apache-2.0 | |
| pipeline_tag: video-text-to-text | |
| # VEGA-3D (Video Extracted Generative Awareness) | |
| VEGA-3D is a plug-and-play framework that repurposes a pre-trained video diffusion model as a Latent World Simulator to enrich Multimodal Large Language Models (MLLMs) with implicit 3D spatial priors for scene understanding, spatial reasoning, and embodied decision making. | |
| More details can be found in the paper: [Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding](https://huggingface.co/papers/2603.19235). | |
| * **Project Page:** [https://h-embodvis.github.io/VEGA-3D/](https://h-embodvis.github.io/VEGA-3D/) | |
| * **Repository:** [https://github.com/H-EmbodVis/VEGA-3D](https://github.com/H-EmbodVis/VEGA-3D) | |
| ## Citation | |
| If you find VEGA-3D useful in your research, please consider citing: | |
| ```bibtex | |
| @inproceedings{wu2026vega, | |
| title={Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding}, | |
| author={Xianjin Wu and Dingkang Liang and Tianrui Feng and Kui Xia and Yumeng Zhang and Xiaofan Li and Xiao Tan and Xiang Bai}, | |
| booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, | |
| year={2026} | |
| } | |
| ``` |