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physics-based Character controllers Using Conditional VAEs

ACM TRANSACTIONS ON GRAPHICS 2022年第4期41卷 p1-12页

作者： Won, Jungdam Gopinath, Deepak Hodgins, Jessica Meta AI Pittsburgh PA 15211 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

High-quality motion capture datasets are now publicly available, and researchers have used them to create kinematics-based controllers that can generate plausible and diverse human motions without conditioning on specific goals (i.e., a task-agnostic generative model). In this paper, we present an algorithm to build such controllers for physically simulated characters having many degrees of freedom. Our physics-based controllers are learned by using conditional VAEs, which can perform a variety of behaviors that are similar to motions in the training dataset. The controllers are robust enough to generate more than a few minutes of motion without conditioning on specific goals and to allow many complex downstream tasks to be solved efficiently. To show the effectiveness of our method, we demonstrate controllers learned from several different motion capture databases and use them to solve a number of downstream tasks that are challenging to learn controllers that generate natural-looking motions from scratch. We also perform ablation studies to demonstrate the importance of the elements of the algorithm.

关键词： Character Animation physics-based simulation and control Motion Capture Reinforcement Learning Variational Autoencoder Behavior Cloning

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Motion In-betweening for Physically Simulated Characters 22

Motion In-betweening for Physically Simulated Characters

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SIGGRAPH Asia Conference

作者： Gopinath, Deepak Joo, Hanbyul Won, Jungdam Meta AI Seattle WA 98164 USA Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450394628

We present a motion in-betweening framework to generate high quality, physically plausible character animation when we are given temporally sparse keyframes as soft animation constraints. More specifically, we learn imitation policies for physically simulated characters by using deep reinforcement learning where the policies can access limited information only. Once learned, the physically simulated characters are capable of adapting to external perturbations while following given sparse input keyframes. We demonstrate the performance of our framework on two different motion datasets and also compare our results with the the results generated by a baseline imitation policy.

关键词： Character Animation physics-based simulation and control Reinforcement Learning Deep Learning Neural Network Multi-agent

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control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

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ACM TRANSACTIONS ON GRAPHICS 2021年第4期40卷 1-11页

作者： Won, Jungdam Gopinath, Deepak Hodgins, Jessica Facebook AI Res Pittsburgh PA 15260 USA

In two-player competitive sports, such as boxing and fencing, athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many degrees-of-freedom. Our framework uses a two step-approach, learning basic skills and learning bout-level strategies, with deep reinforcement learning, which is inspired by the way that people how to learn competitive sports. We develop a policy model based on an encoder-decoder structure that incorporates an autoregressive latent variable, and a mixture-of-experts decoder. To show the effectiveness of our framework, we implemented two competitive sports, boxing and fencing, and demonstrate control policies learned by our framework that can generate both tactical and natural-looking behaviors. We also evaluate the control policies with comparisons to other learning configurations and with ablation studies.

关键词： Character Animation physics-based simulation and control Reinforcement Learning Deep Learning Neural Network Multi-agent

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A Scalable Approach to control Diverse Behaviors for Physically Simulated Characters

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ACM TRANSACTIONS ON GRAPHICS 2020年第4期39卷 33:1–33:12页

作者： Won, Jungdam Gopinath, Deepak Hodgins, Jessica Facebook AI Res Menlo Pk CA 94025 USA

Human characters with a broad range of natural looking and physically realistic behaviors will enable the construction of compelling interactive experiences. In this paper, we develop a technique for learning controllers for a large set of heterogeneous behaviors. By dividing a reference library of motion into clusters of like motions, we are able to construct experts, learned controllers that can reproduce a simulated version of the motions in that cluster. These experts are then combined via a second learning phase, into a general controller with the capability to reproduce any motion in the reference library. We demonstrate the power of this approach by learning the motions produced by a motion graph constructed from eight hours of motion capture data and containing a diverse set of behaviors such as dancing (ballroom and breakdancing), Karate moves, gesturing, walking, and running.

关键词： Character Animation physics-based simulation and control Reinforcement Learning Deep Learning Neural Network Locomotion control

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Learning Body Shape Variation in physics-based Characters

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ACM TRANSACTIONS ON GRAPHICS 2019年第6期38卷 207-207页

作者： Won, Jungdam Lee, Jehee Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

Recently, deep reinforcement learning (DRL) has attracted great attention in designing controllers for physics-based characters. Despite the recent success of DRL, the learned controller is viable for a single character. Changes in body size and proportions require learning controllers from scratch. In this paper, we present a new method of learning parametric controllers for body shape variation. A single parametric controller enables us to simulate and control various characters having different heights, weights, and body proportions. The users are allowed to create new characters through body shape parameters, and they can control the characters immediately. Our characters can also change their body shapes on the fly during simulation. The key to the success of our approach includes the adaptive sampling of body shapes that tackles the challenges in learning parametric controllers, which relies on the marginal value function that measures control capabilities of body shapes. We demonstrate parametric controllers for various physically simulated characters such as bipeds, quadrupeds, and underwater animals.

关键词： Character Animation physics-based simulation and control Reinforcement Learning Deep Learning Neural Network Locomotion control

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