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TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking research paper by Google, 2026

Google · May 12, 2026 · Multimodal and robotics · 3 citations · 37 upvotes · unverified

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What it shows

TrackCraft3R enables efficient dense 3D tracking from monocular video by adapting video diffusion transformers to follow physical points across frames using dual-latent representation and temporal RoPE alignment.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Jisu Nam, Jahyeok Koo, Soowon Son and 4 more
arXiv
2605.12587 · PDF
Venue
arXiv.org
Citations
3, 2 influential · Semantic Scholar
Upvotes
37 · Hugging Face
Code
github.com/cvlab-kaist/TrackCraft3r
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 2Sep 25, 2026
Citationsfirst count: 3Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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