SCA 2021: Eurographics/SIGGRAPH Symposium on Computer Animation

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https://diglib.eg.org/handle/10.2312/2633152

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Browsing SCA 2021: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "character animation"

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    Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model
    (ACM, 2021) Park, Soomin; Jang, Deok-Kyeong; Lee, Sung-Hee; Narain, Rahul and Neff, Michael and Zordan, Victor
    This paper presents a novel deep learning-based framework for translating a motion into various styles within multiple domains. Our framework is a single set of generative adversarial networks that learns stylistic features from a collection of unpaired motion clips with style labels to support mapping between multiple style domains. We construct a spatio-temporal graph to model a motion sequence and employ the spatial-temporal graph convolution networks (ST-GCN) to extract stylistic properties along spatial and temporal dimensions. Through spatial-temporal modeling, our framework shows improved style translation results between significantly different actions and on a long motion sequence containing multiple actions. In addition, we first develop a mapping network for motion stylization that maps a random noise to style, which allows for generating diverse stylization results without using reference motions. Through various experiments, we demonstrate the ability of our method to generate improved results in terms of visual quality, stylistic diversity, and content preservation.
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    A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Control
    (ACM, 2021) Xu, Pei; Karamouzas, Ioannis; Narain, Rahul and Neff, Michael and Zordan, Victor
    We present a simple and intuitive approach for interactive control of physically simulated characters. Our work builds upon generative adversarial networks (GAN) and reinforcement learning, and introduces an imitation learning framework where an ensemble of classifiers and an imitation policy are trained in tandem given pre-processed reference clips. The classifiers are trained to discriminate the reference motion from the motion generated by the imitation policy, while the policy is rewarded for fooling the discriminators. Using our GAN-like approach, multiple motor control policies can be trained separately to imitate different behaviors. In runtime, our system can respond to external control signal provided by the user and interactively switch between different policies. Compared to existing method, our proposed approach has the following attractive properties: 1) achieves state-of-the-art imitation performance without manually designing and fine tuning a reward function; 2) directly controls the character without having to track any target reference pose explicitly or implicitly through a phase state; and 3) supports interactive policy switching without requiring any motion generation or motion matching mechanism. We highlight the applicability of our approach in a range of imitation and interactive control tasks, while also demonstrating its ability to withstand external perturbations as well as to recover balance. Overall, our approach has low runtime cost and can be easily integrated into interactive applications and games.