{"ID":2856856,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10726","arxiv_id":"2510.10726","title":"WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting","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 soon.","short_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 simulta...","url_abs":"https://arxiv.org/abs/2510.10726","url_pdf":"https://arxiv.org/pdf/2510.10726v1","authors":"[\"Yifan Liu\",\"Zhiyuan Min\",\"Zhenwei Wang\",\"Junta Wu\",\"Tengfei Wang\",\"Yixuan Yuan\",\"Yawei Luo\",\"Chunchao Guo\"]","published":"2025-10-12T17:59:09Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}