Synthesizing Physically Plausible Human Motions in 3D Scenes

Synthesizing physically plausible human motions in 3D scenes is a challenging problem. Kinematics-based methods cannot avoid inherent artifacts (e.g., penetration and foot skating) due to the lack of physical constraints. Meanwhile, existing physics-based methods cannot generalize to multi-object scenarios since the policy trained with reinforcement learning has limited modeling capacity. In this work, we present a framework that enables physically simulated characters to perform long-term interaction tasks in diverse, cluttered, and unseen scenes. The key idea is to decompose human-scene interactions into two fundamental processes, Interacting and Navigating, which motivates us to construct two reusable Controller, i.e., InterCon and NavCon. Specifically, InterCon contains two complementary policies that enable characters to enter and leave the interacting state (e.g., sitting on a chair and getting up). To generate interaction with objects at different places, we further design NavCon, a trajectory following policy, to keep characters' locomotion in the free space of 3D scenes. Benefiting from the divide and conquer strategy, we can train the policies in simple environments and generalize to complex multi-object scenes. Experimental results demonstrate that our framework can synthesize physically plausible long-term human motions in complex 3D scenes. Code will be publicly released at https://github.com/liangpan99/InterScene

Game Based Learning for Safety and Security Education

Safety and security education are important part of technology related education, because of recent number of increase in safety and security related incidents. Game based learning is an emerging and rapidly advancing forms of computer-assisted instruction. Game based learning for safety and security education enables students to learn concepts and skills without the risk of physical injury and security breach. In this paper, a pedestal grinder safety game and physical security game have been developed using industrial standard modeling and game development software. The average score of the knowledge test of grinder safety game was 82%, which is higher than traditional lecture only instruction method. In addition, the survey of physical security game shows 84% average satisfaction ratio from high school students who played the game during the summer camp. The results of these studies indicated that game based learning method can enhance students' learning without potential harm to the students

DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent kinematics-based generative motion models offer impressive scalability in modeling extensive motion data, albeit without an interface to reason about and interact with physics. While simulator-in-the-loop learning approaches enable highly physically realistic behaviors, the challenges in training often affect scalability and adoption. We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. DROP can be viewed as a highly stable, minimalist physics-based human simulator that interfaces with a kinematics-based generative motion prior. Utilizing projective dynamics, DROP allows flexible and simple integration of the learned motion prior as one of the projective energies, seamlessly incorporating control provided by the motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP enables us to fully leverage recent advances in generative motion models for physics-based motion synthesis. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.Comment: SIGGRAPH Asia 2023, Video https://youtu.be/tF5WW7qNMLI, Website: https://stanford-tml.github.io/drop