| title | World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible |
|---|---|
| authors | Hao Zhang, Mohamed El Banani, Jen-Hao Cheng, Paul Zhang, Yi Hua, Ben Mildenhall, Christoph Lassner, Narendra Ahuja, Gengshan Yang |
| date | 2026-06-11 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.13652 |
| affiliation | WORLD LABS TECHNOLOGIES INC |
| upvotes | 16 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Image-to-3D methods often trade off faithfulness and completeness: depth estimators are anchored to input pixels but stop at the visible surface, while image-to-3D models generate complete shapes that are often misaligned with the input. We introduce World Tracing, a generative pixel-aligned geometry representation that predicts 3D points aligned with observed pixels while completing geometry beyond the visible surface. For each input pixel, World Tracing predicts an ordered stack of camera-space 3D points, where the first layer represents the visible surface and subsequent layers represent front-to-back intersections with occluded surfaces. We instantiate this representation with a world-tracing diffusion transformer, WT-DiT, which treats multiple geometry layers as separate denoising tokens coupled through factorized and global attention. WT-DiT is trained with pixel-space flow matching and a mixed noise schedule that balances visible-surface reconstruction with occluded-geometry generation. World Tracing achieves strong performance on visible-surface reconstruction and complete geometry generation across object, scene, and dynamic benchmarks, outperforming both depth predictors and image-to-3D generators. It also preserves 2D-to-3D correspondence, enabling text-driven 3D scene editing, geometry-conditioned novel-view video synthesis, and training-free integration with textured-mesh generators. |
cite
@misc{zhang2026world,
title={World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible},
author={Hao Zhang and Mohamed El Banani and Jen-Hao Cheng and Paul Zhang and Yi Hua and Ben Mildenhall and Christoph Lassner and Narendra Ahuja and Gengshan Yang},
year={2026},
eprint={2606.13652},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.13652}
}