Xuefei Cao

Papers

Total Citations

H-Index

About

Xuefei Cao is a researcher in computer vision and machine learning, with a focus on unsupervised motion transfer and image animation. Their most notable contribution is the development of a self-appearance-aided differential evolution framework for motion transfer, which addresses the challenge of transferring motion from a driving video to a static source image while preserving the source identity. This work, published in 2021, has garnered 2 citations and represents a significant step forward in unsupervised methods, which do not require labeled data or domain priors. Cao’s research advances the field of generative models and video synthesis, offering practical applications in animation, virtual reality, and content creation. Their work is particularly valuable for students and researchers interested in bridging the gap between unsupervised learning and high-fidelity motion transfer, contributing to the broader goal of making AI-driven animation more accessible and robust.