World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible

Single-view 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 to the input. We introduce World Tracing, a generative pixel-aligned geometry representation that produces 3D points faithfully reproducing the input image, while containing complete 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 as a world-tracing diffusion transformer (WT-DiT) that treats multiple geometry layers as separate denoising tokens coupled through factorized and global attention. WT-DiT is trained with pixel-space flow matching using a mixed noise schedule to balance reconstruction vs. generation capability. As a result, World Tracing demonstrates strong performance on both visible-surface reconstruction and complete geometry generation across object, scene, and dynamic benchmarks, outperforming both depth predictors and image-to-3D generators. Furthermore, because it preserves 2D-to-3D correspondence, it directly enables text-driven 3D scene editing, geometry-conditioned novel-view video synthesis, and training-free integration with textured-mesh generators.