Normalized Euclidean Distance Matrices for Human Motion Retargeting

Abstract : In character animation, it is often the case that motions created or captured on a specific morphology need to be reused on characters having a different morphology while maintaining specific relationships such as body contacts or spatial relationships between body parts. This process, called motion retargeting, requires determining which body part relationships are important in a given animation. This paper presents a novel frame-based approach to motion retargeting which relies on a normalized representation of body joints distances. We propose to abstract postures by computing all the inter-joint distances of each animation frame and store them in Euclidean Distance Matrices (EDMs). They 1) present the benefits of capturing all the subtle relationships between body parts, 2) can be adapted through a normalization process to create a morphology-independent distance-based representation, and 3) can be used to efficiently compute retargeted joint positions best satisfying newly computed distances. We demonstrate that normalized EDMs can be efficiently applied to a different skeletal morphology by using a Distance Geometry Problem (DGP) approach, and present results on a selection of motions and skeletal morphologies. Our approach opens the door to a new formulation of motion retargeting problems, solely based on a normalized distance representation.
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Antonin Bernardin, Ludovic Hoyet, Antonio Mucherino, Douglas S. Gonçalves, Franck Multon. Normalized Euclidean Distance Matrices for Human Motion Retargeting. MIG 2017 - Motion in Games, Nov 2017, Barcelona, Spain. ⟨10.1145/3136457.3136466⟩. ⟨hal-01632850⟩

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