Outstanding Doctoral Dissertation Award

Launched in 2016, the Doctoral Dissertation Award is awarded annually to recognize a recent doctoral candidate who has successfully defended and completed his or her Ph.D. dissertation in computer graphics and interactive techniques. Recognizing young researchers who have already made a notable contribution very early during their doctoral study, the award is presented each year at the SIGGRAPH Conference and is accompanied by a plaque, complimentary full conference registration and travel to the award ceremony. Honorable Mentions may also be awarded.

Current Recipient

ACM SIGGRAPH is pleased to announce Xue-Bin “Jason” Peng, PhD, as the 2022 recipient of the Outstanding Doctoral Dissertation Award. In his dissertation, Jason presents significant advances in character animation with deep reinforcement learning to produce highly-controllable naturalistic physics-based motion.

Humans and animals are capable of awe-inspiring feats of agility produced by drawing from a vast repertoire of motor skills. In sharp contrast, artificial agents in robotics or computer-generated animation are often stiff and awkward—or else very limited in repertoire—despite decades of progress in the design of controllers. Jason Peng has developed a series of motion imitation and reinforcement learning techniques which upend a decades-long line of research in mocap-based motion synthesis: his work allows agents to learn a large spectrum of highly dynamic and athletic behaviors by mimicking demonstrations. Instead of designing controllers or reward functions for each skill of interest, the agent need only to be provided with a few example motion clips of the desired skill to synthesize a controller, that not only closely replicates the target behavior but can also be robust to perturbations — such as standing up after a fall or reacting to being hit with an object.  The resulting controllers thus reconciliate natural motion and interactivity/response to unpredicted events, marking a significant improvement over previous approaches. His work on adversarial motion priors further shows how to generalize these ideas to large training sets.

In addition to his accomplishments in character animation, Jason has applied his methods to real-world quadrupedal and bipedal robots. He demonstrated that a wide range of learned locomotion gaits can be executed on robot hardware, even enabling a quadruped to chase its own tail. This work beautifully illustrates how to go from real-world data (mocap) to a simulated world for learning the control policies, and then back to the real world for deployment. This kind of interdisciplinary progress is emblematic of the diversity and impact of work developed in the graphics community.

Dr. Peng has advanced the field of controller design through motion imitation and reinforcement learning with a doctoral dissertation in which each chapter develops innovative ideas and progresses towards a steadily increasing level of capability. The SIGGRAPH community thus recognizes Jason Peng for these extraordinary achievements with the 2022 ACM SIGGRAPH Doctoral Dissertation Award, and looks forward to his future contributions.

Nomination Procedure

All doctoral dissertations successfully defended (or thesis accepted) during the calendar year prior to the nomination deadline are eligible for consideration. There is no limit on the number of nominations that can be made from any single institution or advisor. The key criteria used to evaluate the nominations include technical depth, significance of the research contribution, potential impact on theory and practice, and quality of presentation.

The submitted dissertation should be a finalized version. Nominations are welcomed from any country, but only English language versions will be accepted. Nominations are evaluated by the Outstanding Doctoral Dissertation Award Committee. Nominations, including all supporting materials and endorsement letters, are due by January 31 of each year. Click the button below to submit a nomination.

Requirements