AI RESEARCH
TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking
arXiv CS.CV
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ArXi:2605.12587v1 Announce Type: new Dense 3D tracking from monocular video is fundamental to dynamic scene understanding. While recent 3D foundation models provide reliable per-frame geometry, recovering object motion in this geometry remains challenging and benefits from strong motion priors learned from real-world videos. Existing 3D trackers either follow iterative paradigms trained from scratch on synthetic data or fine-tune 3D reconstruction models learned from static multi-view images, both lacking real-world motion priors.