[Paper] World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible
Source: arXiv - 2606.13652v1
Overview
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.
Key Contributions
This paper presents research in the following areas:
- cs.CV
- cs.GR
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.CV.
Authors
- Hao Zhang
- Mohamed El Banani
- Jen-Hao Cheng
- Paul Zhang
- Yi Hua
- Ben Mildenhall
- Christoph Lassner
- Narendra Ahuja
- Gengshan Yang
Paper Information
- arXiv ID: 2606.13652v1
- Categories: cs.CV, cs.GR
- Published: June 11, 2026
- PDF: Download PDF