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Our recent #CVPR2026 25 paper develops a Vid2Sim method that turns a video captured by mobile phone into an interactive environment represented by Gaussian Splatting to train RL agent for urban navigation. Incredible Ziyang Xie leaded the project. Webpage:

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Bolei Zhou

8,896 subscribers

31,701 Aufrufe • vor 1 Jahr •via X (Twitter)

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8 Kommentare

Profilbild von Zhengzhong Tu
Zhengzhong Tuvor 1 Jahr

@CVPR @ZiyangXie_ Awesome work!

Profilbild von AssemblyAI
AssemblyAIvor 1 Jahr

Announcing: Our most advanced speech-to-text model goes beyond accuracy to capture the real-world complexity of human conversation and deliver reliable, source-of-truth audio data. Explore Universal-2 updates 👇

Profilbild von Daniel
Danielvor 1 Jahr

@CVPR @ZiyangXie_ 🚀🚀

Profilbild von Markus Wulfmeier
Markus Wulfmeiervor 1 Jahr

@CVPR @ZiyangXie_ Nice work!

Profilbild von Ajay Divakaran
Ajay Divakaranvor 1 Jahr

@CVPR @ZiyangXie_ Very nice work Bolei

Profilbild von Bolei Zhou
Bolei Zhouvor 1 Jahr

@CVPR @ZiyangXie_ Thank you Ajay!

Profilbild von ryan yang
ryan yangvor 1 Jahr

@CVPR @ZiyangXie_ Mobile vid→sim? Modular wins. Pilot, iterate. RL needs real-world data.

Profilbild von Maya N
Maya Nvor 1 Jahr

@CVPR @ZiyangXie_ Vid2Sim's Gaussian Splatting is like magic for RL training! Now if it can manage a meet-up with my AI buddies in an urban maze, we're set! 🚴 Incredible work, pushing sim-to-real boundaries. 👏

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