Browsing by Author "Multon, Franck"

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    3D Human Shape and Pose from a Single Depth Image with Deep Dense Correspondence Enabled Model Fitting
    (The Eurographics Association, 2022) Wang, Xiaofang; Boukhayma, Adnane; Prévost, Stéphanie; Desjardin, Eric; Loscos, Celine; Multon, Franck; Sauvage, Basile; Hasic-Telalovic, Jasminka
    We propose a two-stage hybrid method, with no initialization, for 3D human shape and pose estimation from a single depth image, combining the benefits of deep learning and optimization. First, a convolutional neural network predicts pixel-wise dense semantic correspondences to a template geometry, in the form of body part segmentation labels and normalized canonical geometry vertex coordinates. Using these two outputs, pixel-to-vertex correspondences are computed in a six-dimensional embedding of the template geometry through nearest neighbor. Second, a parametric shape model (SMPL) is fitted to the depth data by minimizing vertex distances to the input. Extensive evaluation on both real and synthetic human shape in motion datasets shows that our method yields quantitatively and qualitatively satisfactory results and state-of-the-art reconstruction errors.
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    MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
    (ACM Association for Computing Machinery, 2023) Younes, Mohamed; Kijak, Ewa; Kulpa, Richard; Malinowski, Simon; Multon, Franck; Wang, Huamin; Ye, Yuting; Victor Zordan
    Simulating realistic interaction and motions for physics-based characters is of great interest for interactive applications, and automatic secondary character animation in the movie and video game industries. Recent works in reinforcement learning have proposed impressive results for single character simulation, especially the ones that use imitation learning based techniques. However, imitating multiple characters interactions and motions requires to also model their interactions. In this paper, we propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach that generalizes the idea of motion imitation for one character to deal with both the interaction and the motions of the multiple physics-based characters. Two unstructured datasets are given as inputs: 1) a single-actor dataset containing motions of a single actor performing a set of motions linked to a specific application, and 2) an interaction dataset containing a few examples of interactions between multiple actors. Based on these datasets, our system trains control policies allowing each character to imitate the interactive skills associated with each actor, while preserving the intrinsic style. This approach has been tested on two different fighting styles, boxing and full-body martial art, to demonstrate the ability of the method to imitate different styles.
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    Safeguarding our Dance Cultural Heritage
    (The Eurographics Association, 2022) Aristidou, Andreas; Chalmers, Alan; Chrysanthou, Yiorgos; Loscos, Celine; Multon, Franck; Parkins, J. E.; Sarupuri, Bhuvan; Stavrakis, Efstathios; Hahmann, Stefanie; Patow, Gustavo A.
    Folk dancing is a key aspect of intangible cultural heritage that often reflects the socio-cultural and political influences prevailing in different periods and nations; each dance produces a meaning, a story with the help of music, costumes and dance moves. It has been transmitted from generation to generation, and to different countries, mainly due to movements of people carrying and disseminating their civilization. However, folk dancing, amongst other intangible heritage, is at high risk of disappearing due to wars, the moving of populations, economic crises, modernization, but most importantly, because these fragile creations have been modified over time through the process of collective recreation, and/or changes in the way of life. In this tutorial, we show how the European Project, SCHEDAR, exploited emerging technologies to digitize, analyze, and holistically document our intangible heritage creations, that is a critical necessity for the preservation and the continuity of our identity as Europeans.