Disentangling Subject-Dependent/-Independent Representations for 2D Motion Retargeting

We consider the problem of 2D motion retargeting, which is to transfer the motion of one 2D skeleton to another skeleton of a different body shape. Existing methods decompose the input motion skeleton into dynamic (motion) and static (body shape, viewpoint angle, and emotion) features and synthesize a new skeleton by mixing up the features extracted from the different data. However, the resulting motion skeletons do not reflect subject-dependent factors that can stylize motion, such as skill and expressions, leading to unattractive results. In this work, we propose a novel network to separate subject-dependent and -independent motion features and to reconstruct a new skeleton with or without subject-dependent motion features. The core of our approach is adversarial feature disentanglement. The motion features and a subject classifier are trained simultaneously such that subject-dependent motion features do allow for between-subject discrimination, whereas subject-independent features cannot. The presence or absence of individuality is readily controlled by a simple summation of the motion features. Our method shows superior performance to the state-of-the-art method in terms of reconstruction error and can generate new skeletons while maintaining individuality.