It's time to scale!
Introducing Light-O1, a whole-body intelligence model scaled through human action pretraining.
• Human action at scale: We learn from structured human actions recovered from internet videos, capturing a scale and diversity that robot data collection alone struggles to match.
• Cross-embodiment transfer scaling law: Scaling human action pretraining yields power-law reductions in prediction error across embodiments.
• Whole-body intelligence: Adapted to different humanoids, Light-O1 coordinates movement, posture, and manipulation to autonomously perform household tasks.
Mark Yu retweeted
Our humanoid robot vaults, climbs, and crosses terrain it’s never seen before—deciding which move to use in real time, completely on its own 🤖 Real → sim → real. 👇
Mark Yu retweeted
🚨 We just released 🎞️MoVieS — a feed-forward model that reconstructs 4D scenes in ⚡️1 second
My favorite part: It learns dense (pixel-wise) sharp 3D world movements from novel view rendering + sparse point tracking supervision 🤯🎯
Check it out 👉 chenguolin.github.io/project…
Mark Yu retweeted
📢I'm pleased to introduce GlueStick: A robust image matcher that combines points and lines📢
➡️Website: iago-suarez.com/gluestick
➡️Code: github.com/cvg/GlueStick
➡️Paper: arxiv.org/pdf/2304.02008.pdf
This is a collaboration with Rémi Pautrat, Yifan Yu, @mapo1 and @visionviktor.
1/🧵
Mark Yu retweeted
3D Line Mapping Revisited
@B1ueber2y, Yifan Yu, Rémi Pautrat, @mapo1 , @visionviktor
tl;dr: Point-guided line SfM with scale-invariant scoring -> profit. Even improves SuperPoint + Colmap bundle adjustment
arxiv.org/abs/2303.17504.pdf









