Section: New Results

Virtual human simulation

Novel Distance Geometry based approaches for Human Motion Retargeting

Since September 2016, Antonio Mucherino has a half-time Inria detachment in the MimeTIC team, in order to collaborate on exploring distance geometry-based problems in representing and editing human motion.

In this context, an extension of a distance geometry approach to dynamical problems was proposed in [23], and we co-supervised Antonin Bernardin for his Master thesis, which focused on applying such extended approach for retargeting human motions. 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. However, specific relationships such as body contacts or spatial relationships between body parts are often lost during this process, and existing approaches typically try to determine automatically which body part relationships should be preserved in such animation. Instead, we proposed a novel frame-based approach to motion retargeting [18], [22] which relies on a normalized representation of all the body joints distances to encompass all the relationships existing in a given motion. In particular, we proposed to abstract postures by computing all the inter-joint distances of each animation frame and to represent them by Euclidean Distance Matrices (EDMs). Such EDMs present the benefits of capturing all the subtle relationships between body parts, while being adaptable through a normalization process to create a morphology independent distance-based representation. Finally, they can also be used to efficiently compute retargeted joint positions best satisfying newly imposed distances. We demonstrated that normalized EDMs can be efficiently applied to a different skeletal morphology by using a dynamical distance geometry approach, and presented results on a selection of motions and skeletal morphologies.

In parallel, in collaboration with national (LIX, École Polytechnique, Palaiseau) and international partners, we have been working for improving the performances of existing algorithms for distance geometry, independently from the considered application. In [4], we analyzed the main causes for the approach to fail to provide accurate solutions in cases where interval distances are provided (instead of unique distance values), and we proposed some possible strategies to detect such situations. In [27], we presented a linear optimization problem for a common pre-processing step in distance geometry: the one of identifying a special vertex order allowing to discretize the solution search space.