Computer Graphics Achievement Award

The computer graphics achievement award is given to an individual for outstanding achievement in computer graphics and interactive techniques. The award includes a prize of $2,000.

Current Recipient

Aaron Hertzmann

For his pioneering work in non-photorealistic animation and rendering (NPR), image synthesis, character animation, computational photography and the interplay between computer generated and traditional art

ACM SIGGRAPH is pleased to present the 2024 Achievement Award to Aaron Hertzmann for his pioneering work in non-photorealistic animation and rendering (NPR), image synthesis, character animation, computational photography, and the interplay between computer-generated and traditional art.

Aaron’s groundbreaking contributions to the field of NPR are widely recognized, particularly his early work on painterly rendering of images and video. His innovative techniques for line drawing of 3D models continue to influence the field today. Aaron’s seminal Image Analogies paper spearheaded a new era of machine learning applied to image authoring and stylization. Its long-lasting impact extends to recent image editing methods using Generative Adversarial Networks and diffusion models.

As one of the pioneering researchers at the intersection of computer graphics and machine learning, Aaron has made significant contributions to character animation with models learned from motion-capture data, as well as capturing non-rigid motion from video and rotoscoping. In addition to ML-based approaches, Aaron’s work in physics-based human motion control laid the groundwork for the subsequent rise of reinforcement learning for physics-based animation. In the realm of computational photography and image editing, Aaron’s contributions include advancements in image deblurring, portrait segmentation, depth-aware editing, and GAN controls. Aaron also introduced early machine learning approaches for graphic design and for geometry processing.

Beyond his technical contributions, Aaron is recognized as an influential thinker across computer graphics and computer vision. His position articles on the science of art and generative AI art offer valuable insights into the evolving landscape of art-making processes, drawing from art history, philosophy, and psychology. Moreover, Aaron’s theories on human visual perception of pictures have challenged conventional understanding, providing explanations for phenomena unexplained by existing linear perspective theories. Aaron’s intellectual contributions have sparked debates and discussions across the interdisciplinary communities of computer science and art, enriching our understanding of the complex relationship between technology and creativity.

Aaron received a Bachelor degree in Computer Science and Art at Rice University and a Ph.D. degree in Computer Science from New York University in 2001 under the supervision of Ken Perlin and Denis Zorin. He was a Professor of Computer Science at the University of Toronto from 2003-2013, and since 2012 has been at Adobe, where he became a Principal Scientist in 2016. Throughout his career, Aaron has consistently provided his professional services to his communities, including as co-organizer of NPAR 2004, as the inaugural SIGGRAPH 2022 Conference Papers Director for the conference track that he advocated for, and as the Editor-in-Chief of Foundations and Trends in Computer Graphics and Vision.

Nomination Procedure

ACM SIGGRAPH members are encouraged to nominate individuals for the Computer Graphics Achievement Award by sending an email to the Technical Awards Chair (technical_awards@siggraph.org) by January 31 of each year.

Requirements

Your nomination should describe a candidate’s most significant research contribution(s), and optionally describe industrial impact, community service, and/or other contributions to computer graphics and interactive techniques. The Technical Awards Committee uses nomination statements as the main basis for their selections, so a concise and clear statement is strongly encouraged. Descriptions of a small number of contributions (one is acceptable) are preferable to a long list of activities.