Explorable 3D world generation from a single image or text prompt forms a cornerstone of spatial intelligence. Recent works utilize video model to achieve wide-scope and generalizable 3D world generation. However, existing approaches often suffer from a limited scope in the generated scenes. In this work, we propose Matrix-3D, a framework that utilize panoramic representation for wide-coverage omnidirectional explorable 3D world generation that combines conditional video generation and panoramic 3D reconstruction. We first train a trajectory-guided panoramic video diffusion model that employs scene mesh renders as condition, to enable high-quality and geometrically consistent scene video generation. To lift the panorama scene video to 3D world, we propose two separate methods: (1) a feed-forward large panorama reconstruction model for rapid 3D scene reconstruction and (2) an optimization-based pipeline for accurate and detailed 3D scene reconstruction. To facilitate effective training, we also introduce the Matrix-Pano dataset, the first large-scale synthetic collection comprising 116K high-quality static panoramic video sequences with depth and trajectory annotations. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance in panoramic video generation and 3D world generation. See more in https://matrix-3d.github.io.
@article{arxiv.2508.08086,
title = {Matrix-3D: Omnidirectional Explorable 3D World Generation},
author = {Zhongqi Yang and Wenhang Ge and Yuqi Li and Jiaqi Chen and Haoyuan Li and Mengyin An and Fei Kang and Hua Xue and Baixin Xu and Yuyang Yin and Eric Li and Yang Liu and Yikai Wang and Hao-Xiang Guo and Yahui Zhou},
journal= {arXiv preprint arXiv:2508.08086},
year = {2025}
}