Advanced Modeling of Human Movement for Computer Animation

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Computer animation has influenced the world not only through Hollywood blockbuster CG (Computer Graphics) movies and video games, but also via virtual reality applications that train engineers and doctors with virtual tools in simulated environments. Despite the huge success of computer animation in general, 3D Character Animation still relies mostly on motion capture and thus remains labor-intensive and costly. The making of Avatar took dozens of visual effects companies and thousands of digital artists years on performance capture of real human actors, and manual editing of the captured animations afterwards if any changes are needed. We deem character animation the current bottleneck of computer graphics and creative content creation. ******I propose to model human movement with a holistic approach to firstly deepen our understanding of human motion, and secondly enable CG professionals and novice users alike to create richer and more-realistic human motions with ease. For instance, given a 5-minute motion capture data of human movement, can we automatically equip a digital avatar to move about and accomplish tasks with all the skills seen in the data? To achieve this overall goal, we propose three specific short-term objectives: (1) deeply analyze and understand motion capture datasets, (2) learn control models at individual skill-level at interactive rates, and (3) develop a skill manager to equip digital avatars with human-level motion intelligence. I plan to develop novel algorithms that combine model-based control methods with recent deep-learning based model-free approaches. I also intend to investigate multiple learning methods. It is not obvious how deep learning can transform problem solving in computer animation in general, compared to what happened in computer vision, as there are significant differences between the two fields. I wish to contribute to answering this question with my research for the five years to come.******Modeling of human movement is a fundamental problem in computer animation, computer vision, biomechanics, and humanoid robotics. Therefore, many non-CG applications can benefit from advanced modeling of human movement as well, such as activity recognition in video surveillance, diagnosis and rehabilitation of injured patients, and humanoid robot control and interaction. Thus although studied in a computer animation context, we expect much broader scientific impact of this research in computer science, engineering, and medical disciplines. The results of this research are directly transferable to animation tools and systems for movie, game, and VR industries. We thus also expect economic impact from the proposed research. In addition, this research will likely generate social impacts as well, as it will enable non-experts to create animation content in easy and intuitive ways as we shoot photos and videos today.