Jiaman Li's banner

Jiaman Li

@jiaman01 • 1,295 subscribers

Applied Scientist at Amazon Frontiers AI and Robotics (FAR), Stanford PhD, Human Motion Modeling, Humanoid Root Learning

Shorts

#ECCV24 We introduce Controllable Human-Object Interaction Synthesis (CHOIS) to generate interactions given text in a 3D scene. w alex, Roozbeh Mottaghi Jiajun Wu Xavier Puig Karen. code: project page:

#ECCV24 We introduce Controllable Human-Object Interaction Synthesis (CHOIS) to generate interactions given text in a 3D scene. w alex, Roozbeh Mottaghi Jiajun Wu Xavier Puig Karen. code: project page:

13,185 次观看

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

🤖 Introducing Human-Object Interaction from Human-Level Instructions! First complete system that generates physically plausible, long-horizon human-object interactions with finger motions in contextual environments, driven by human-level instructions. 🔍 Our approach: - LLMs transform instructions into execution plans & target layouts - Multi-stage framework generates synchronized object, body & finger motions - RL policy tracks generated motions in physics simulation Project: Demo: Paper:

🤖 Introducing Human-Object Interaction from Human-Level Instructions! First complete system that generates physically plausible, long-horizon human-object interactions with finger motions in contextual environments, driven by human-level instructions. 🔍 Our approach: - LLMs transform instructions into execution plans & target layouts - Multi-stage framework generates synchronized object, body & finger motions - RL policy tracks generated motions in physics simulation Project: Demo: Paper:

104,574 次观看 • 1 年前

🔥 Introducing MVLift: Generate realistic 3D motion without any 3D training data - just using 2D poses from monocular videos! Applicable to human motion, human-object interaction & animal motion. Joint work w/ Jiajun Wu & Karen 💡 How? We reformulate 3D motion estimation as generating consistent multi-view 2D pose sequences. Our framework uses 2D motion diffusion to progressively establish multi-view consistency, requiring only single-view 2D pose sequences for training. Project: Video with demonstration: Paper:

🔥 Introducing MVLift: Generate realistic 3D motion without any 3D training data - just using 2D poses from monocular videos! Applicable to human motion, human-object interaction & animal motion. Joint work w/ Jiajun Wu & Karen 💡 How? We reformulate 3D motion estimation as generating consistent multi-view 2D pose sequences. Our framework uses 2D motion diffusion to progressively establish multi-view consistency, requiring only single-view 2D pose sequences for training. Project: Video with demonstration: Paper:

15,788 次观看 • 1 年前

没有更多内容可加载