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@patrickamadeus_

phd @mbzuai | multimodal in research & life.
patrickamadeus.github.io Joined November 2020
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patrick Verified account @patrickamadeus_
πŸ€–πŸ€š Give a video generation model a scene and a goal. Can it generate egocentric execution to get the job done?

We tested 110 real-world scenarios on 6 SoTA models to see how they perform.

Introducing Ego2Act, a goal-directed video generation benchmark πŸ‘‡
🌐 ego2act.github.io
πŸ§ͺ alphaxiv.org/abs/2610.01092
πŸ€— huggingface.co/datasets/ego2a…

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patrick Verified account @patrickamadeus_
Bangers as usual

Excited to try this on ego2act.github.io !
ego2act.github.io Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation Ego2Act is a benchmark of 110 real-world egocentric manipulation tasks and 2,640 videos testing whether video generators can carry out a goal, scored by the reference-free Ego2ActJudge.
Institute of Foundation Models Verified account @IFM_AI 13h
Introducing PAN 2: an open foundation world model for physical intelligence.

A language model predicts the next word. PAN 2 predicts the next world: what happens after each action, grounded in time, physics and space.

Towards AI that simulates β€œwhat if?” before it acts.

More details: ifm.ai/pan/
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patrick Verified account @patrickamadeus_
We worked together on ego2act.github.io collab between @mbzuai and @unc_ai_group.

Even though it was remote, the project carried through smoothly thanks to her thorough yet on-point mentorship!

Any institution would be lucky to onboard Dr. Yue 😁
ego2act.github.io Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation Ego2Act is a benchmark of 110 real-world egocentric manipulation tasks and 2,640 videos testing whether video generators can carry out a goal, scored by the reference-free Ego2ActJudge.
Yue Zhang Verified account @zhan1624 Nov 3
πŸš€ I'm on the 2026–27 faculty job market and would love to connect!

🌟 I recently had the pleasure of speaking as a Rising Star at the 2026 Michigan AI Symposium, sharing my research on spatial intelligence: how embodied agents understand their surroundings, imagine beyond observations with world models, and learn from failures to improve.

It was wonderful to meet and exchange ideas with so many inspiring researchers!

Huge thanks to @mohitban47 for his continuous support, to my amazing collaborators and labmates @unc_ai_group, and to the organizers @michigan_AI for this wonderful opportunity!
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