Kaihao Zhang

Papers

Total Citations

H-Index

About

Kaihao Zhang is a rising researcher in computer vision and human motion analysis, with a focus on generative models and temporal prediction. His work centers on developing diffusion-based frameworks for complex motion forecasting, particularly in hand motion prediction—a critical area for human-computer interaction, robotics, and augmented reality. His most-cited paper, "Prompting Future Driven Diffusion Model for Hand Motion Prediction" (2024), introduces an innovative approach that leverages future-conditioned prompts to guide diffusion processes, enabling more accurate and context-aware hand trajectory forecasts. This work has already garnered early attention with 4 citations, signaling its potential impact in the field. Zhang’s contributions advance the integration of prompt engineering with generative diffusion models, addressing challenges in long-term motion coherence and real-time applicability. His research bridges the gap between high-level semantic cues and low-level motion dynamics, offering new pathways for interactive AI systems. As an emerging scholar, Zhang’s work is poised to influence both theoretical understanding and practical deployment of motion prediction technologies, making him a notable figure to watch in the evolving landscape of computer vision and embodied AI.