Jiaman Li

Papers

Total Citations

115

H-Index

About

Jiaman Li is a leading researcher at the intersection of computer vision, computer graphics, and embodied AI, with a core focus on human motion prediction and synthesis within contextual environments. Her work is pivotal for advancing character animation, assistive robotics, and AR/VR applications. Li’s major contributions center on integrating scene context and human intention into motion models. Her highly cited paper, "GIMO: Gaze-Informed Human Motion Prediction in Context" (2022, 59 citations), pioneered the use of human gaze as a key signal for intention, enabling more accurate and safe motion prediction for human-robot interaction. She further advanced the field with "Object Motion Guided Human Motion Synthesis" (2023, 54 citations), which models the intricate interplay between a person’s actions and the objects they manipulate, a critical step for realistic task completion in virtual and physical spaces. By grounding motion in both gaze and object dynamics, Li’s work provides a foundational framework for creating more intelligent, context-aware agents that can anticipate and collaborate with humans seamlessly.