The 2024 Asiagraphics (AG) Outstanding Technical Contributions Award was presented to Dr. Xin Tong from Microsoft Research Asia and Anuttacon, USA. The winner of this award was selected by the award jury chaired by Prof. Ming Lin (UMD College Park) and Prof. Leif Kobbelt (RWTH Aachen).
Dr. Xin Tong was a Partner Research Manager in Microsoft Research Asia (MSRA). He joined MSRA since 1999 after obtaining a PhD degree from Tsinghua University. He received Bachler and Master degrees from Zhejiang University in 1993, and 1996 respectively. His research interests include appearance modeling, texture synthesis, forward and inverse light transport, facial animation, as well as 3D deep learning.
Xin has enriched our field of computer graphics with outstanding contributions that advanced the theory and practice of computer graphics and influenced the direction of graphics research, especially in data-driven appearance modeling, rendering and acquisition, as well as image-based relighting. His works on high-dimensional texture representations and associated synthesis and rendering techniques have made fundamental contributions to modeling and visualizing the sophisticated appearances of materials with an unprecedented level of realism and efficiency. Xin introduces the data-driven method in surface reflectance acquisition and has made essential contributions to all aspects of this area, including data-driven representation, reconstruction algorithm, and hardware design and setup. Xin’s works on neural-network based light transport profoundly improve the efficiency and practicality of image-based relighting and have inspired many follow-ups including recent neural-network based scene representations and rendering methods.
Xin is also well recognized as a leader in the graphics community and actively served for graphics conference and activities in Asia. He was the paper co-chair of PG 2013 and the chair of ACM SIGGRAPH ASIA Technical Briefs and Posters 2019. He has served or is serving as an associate editor of ACM TOG, IEEE TVCG, CGF, IEEE CG&A, and CVMJ.
