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This paper introduces robot-centric pointmaps, which encode 3D scene coordinates in the robot frame directly into image pixels, to resolve the frame mismatch between camera observations and robot action definitions in Vision-Language-Action models. The method improves Pi0.5 and SmolVLA on RoboCasa and generalizes better to unseen camera placements in real-robot experiments.
World Tracing introduces a generative pixel-aligned geometry representation that predicts 3D points aligned with observed pixels while completing occluded surfaces. It uses a diffusion transformer trained with pixel-space flow matching, achieving strong performance on visible-surface reconstruction and complete geometry generation across object, scene, and dynamic benchmarks.
ViGeo is a transformer-based foundation model that recovers dense and consistent 3D geometry from videos using dynamic chunking attention and a completion-based data refinement framework, achieving state-of-the-art performance across multiple tasks.