Les environnements virtuels permettent de représenter des personnes par des humains virtuels ou avatars. Le sentiment de présence virtuelle entre utilisateurs est renforcé lorsque l’avatar ressemble à la personne qu’il représente. L’avatar est alors classiquement un clone de l’utilisateur qui reproduit son apparence et sa voix. Toutefois, la possibilité de cloner l’expressivité des gestes d’une personne a reçu peu d’attention jusqu’ici. Expressivité gestuelle combine le style et l’humeur d’une personne. Des paramètres décrivant l’expressivité ont été proposés dans des travaux antérieurs pour animer les agents conversationnels. Dans ce travail, nous nous intéressons à l’expressivité des mouvements du poignet. Tout d’abord, nous proposons des algorithmes pour estimer trois paramètres d’expressivité à partir des trajectoires dans l’espace du poignet : la répétition, l’étendue spatiale et l’étendue temporelle. Puis, nous avons mené une étude perceptive sur la pertinence de l’expressivité des gestes pour reconnaître des personnes. Nous avons animé un agent virtuel en utilisant l’expressivité estimée de personnes réelles, et évalué si des utilisateurs peuvent reconnaître ces personnes à partir des animations. Nous avons constaté que des gestes répétitifs dans l’animation constituent une caractéristique discriminante pour reconnaître les personnes, tandis que l’absence de répétition est associée à des personnes qui répètent des gestes ou non. Plus important, nous avons trouvé que 75% ou plus des utilisateurs peuvent reconnaître une personne (parmi deux proposée) à partir d’animations virtuelles qui ne diffèrent que par leurs étendues spatiales et temporelles. L’expressivité gestuelle apparaît donc comme un nouvel indice pertinent pour le clonage d’une personne / Virtual environments allow human beings to be represented by virtual humans or avatars. Users can share a sense of virtual presence is the avatar looks like the real human it represents. This classically involves turning the avatar into a clone with the real human’s appearance and voice. However, the possibility of cloning the gesture expressivity of a real person has received little attention so far. Gesture expressivity combines the style and mood of a person. Expressivity parameters have been defined in earlier works for animating embodied conversational agents.In this work, we focus on expressivity in wrist motion. First, we propose algorithms to estimate three expressivity parameters from captured wrist 3D trajectories: repetition, spatial extent and temporal extent. Then, we conducted perceptual study through a user survey the relevance of expressivity for recognizing individual human. We have animated a virtual agent using the expressivity estimated from individual humans, and users have been asked whether they can recognize the individual human behind each animation. We found that, in case gestures are repeated in the animation, this is perceived by users as a discriminative feature to recognize humans, while the absence of repetition would be matched with any human, regardless whether they repeat gesture or not. More importantly, we found that 75 % or more of users could recognize the real human (out of two proposed) from an animated virtual avatar based only on the spatial and temporal extents. Consequently, gesture expressivity is a relevant clue for cloning. It can be used as another element in the development of a virtual clone that represents a person

Virtual environments allow human beings to be represented by virtual humans or avatars. Users can share a sense of virtual presence is the avatar looks like the real human it represents. This classically involves turning the avatar into a clone with the real human's appearance and voice. However, the possibility of cloning the gesture expressivity of a real person has received little attention so far. Gesture expressivity combines the style and mood of a person. Expressivity parameters have been defined in earlier works for animating embodied conversational agents.In this work, we focus on expressivity in wrist motion. First, we propose algorithms to estimate three expressivity parameters from captured wrist 3D trajectories: repetition, spatial extent and temporal extent. Then, we conducted perceptual study through a user survey the relevance of expressivity for recognizing individual human. We have animated a virtual agent using the expressivity estimated from individual humans, and users have been asked whether they can recognize the individual human behind each animation. We found that, in case gestures are repeated in the animation, this is perceived by users as a discriminative feature to recognize humans, while the absence of repetition would be matched with any human, regardless whether they repeat gesture or not. More importantly, we found that 75 % or more of users could recognize the real human (out of two proposed) from an animated virtual avatar based only on the spatial and temporal extents. Consequently, gesture expressivity is a relevant clue for cloning. It can be used as another element in the development of a virtual clone that represents a person

Human motion analysis is one of the major problems in computer vision research. It deals with the study of the motion of human body in video data from different aspects, ranging from the tracking of body parts and reconstruction of 3D human body configuration, to higher level of interpretation of human action and activities in image sequences. When human motion is observed through video camera, it is perspectively distorted and may appear totally different from different viewpoints. Therefore it is highly challenging to establish correct relationships between human motions across video sequences with different camera settings. In this work, we investigate the geometric invariance in the motion of human body, which is critical to accurately understand human motion in video data regardless of variations in camera parameters and viewpoints. In human action analysis, the representation of human action is a very important issue, and it usually determines the nature of the solutions, including their limits in resolving the problem. Unlike existing research that study human motion as a whole 2D/3D object or a sequence of postures, we study human motion as a sequence of body pose transitions. We also decompose a human body pose further into a number of body point triplets, and break down a pose transition into the transition of a set of body point triplets. In this way the study of complex non-rigid motion of human body is reduced to that of the motion of rigid body point triplets, i.e. a collection of planes in motion. As a result, projective geometry and linear algebra can be applied to explore the geometric invariance in human motion. Based on this formulation, we have discovered the fundamental ratio invariant and the eigenvalue equality invariant in human motion. We also propose solutions based on these geometric invariants to the problems of view-invariant recognition of human postures and actions, as well as analysis of human motion styles. These invariants and their applicability have been validated by experimental results supporting that their effectiveness in understanding human motion with various camera parameters and viewpoints.

在這個研究中，我們嘗試將節奏的要素（速度、誇張度與時間調配）參數化，以產生能控制特定風格之人物角色的動畫。角色動作風格化的生成及控制是藉由一個層級式的動畫控制系統RhyCAP (Rhythmic Character Animation Playacting system), 透過一個節奏動作控制（Rhythmic Motion Control, RMC) 的方法來實現。RMC是基於傳統動畫的原則，設計參數化的動作指令，來產生生動並具有說服力的角色動作。此外，RMC也提供了運動行為的模型來控制角色動畫的演出。藉由RhyCAP系統所提供的高階控制介面，即使是沒有經過專業傳統動畫技巧訓練的使用者，也能夠創作出戲劇性的中國舞獅動畫。 / In this research, we attempt to parameterize the rhythmic factors (tempo, exaggeration and timing) into the generation of controllable stylistic character animation. The stylized character motions are generated by a hierarchical animation control system, RhyCAP (Rhythmic Character Animation Playacting system) and realized through an RMC (Rhythmic Motion Control) scheme. The RMC scheme can generate convincible and expressive character motions from versatile action commands with the rhythmic parameters defined according to the principles of traditional animation. Besides, RMC also provide controllable behavior models to enact the characters. By using the high-level control interface of the RhyCAP system, the user is able to create a dramatic Chinese Lion Dance animation intuitively even though he may not be professionally trained with traditional animation skills.