{'fraction_non_alphanumeric': 0.021251475796930343, 'fraction_numerical': 0.004722550177095631, 'mean_word_length': 5.37593984962406, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A virtual reality setup for controllable, stylized real-time interactions between humans and avatars with sparse Gaussian process dynamical models

Building on our previous work [Taubert et al. 2012], we present an approach for real-time interaction between a real human and an avatar. We generate reactive motions by a dynamical extension of a hierarchical Gaussian process latent variable model, including latent dimensions for emotional style variation and target positions. This allows the avatar to produce accurate reactive motions to the human. To validate our approach, we developed a real-time application where an avatar and a human actor engage in emotional 'high fives'. Furthermore, we show preliminary results indicating that humans do perceive emotions more accurately when engaging in interaction as opposed to passive observation.