Title: WorldMirror: Universal  3D World Reconstruction with Any-Prior Prompting
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Feed-Forward 3D Reconstruction, Feed-Forward Gaussian Splatting

Abstract: We present WorldMirror, an all-in-one, feed-forward model for versatile 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. This elegant and unified architecture leverages available prior information to resolve structural ambiguities and delivers geometrically consistent 3D outputs in a single forward pass. 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.

TL;DR: We introduce WorldMirror, a unified feed-forward model that accepts flexible inputs (images with optional geometric priors) and generates comprehensive 3D representations.