WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting
Yifan Liu, Zhiyuan Min, Zhenwei Wang, Junta Wu, Tengfei Wang, Yixuan Yuan, Yawei Luo, Chunchao Guo
Abstract
We present WorldMirror, a unified feed-forward model for comprehensive 3D geometric prediction tasks. Unlike existing methods constrained to image-only inputs or customized for a specific task, our framework flexibly integrates diverse geometric priors, including camera poses, intrinsics, and depth maps, while simultaneously generating multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussians. Remarkably, prior injection yields universal gains across all tasks, suggesting that input flexibility and multi-task prediction are mutually reinforcing. WorldMirror achieves state-of-the-art performance across diverse benchmarks from camera, point map, depth, and surface normal estimation to novel view synthesis, while maintaining the efficiency of feed-forward inference. Code and models will be publicly available.
BibTeX
@inproceedings{
liu2026worldmirror,
title={WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting},
author={Yifan Liu and Zhiyuan Min and Zhenwei Wang and Junta Wu and Tengfei Wang and Yixuan Yuan and Yawei Luo and Chunchao Guo},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=HFNJOpXHfm}
}