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Human motion indexing and retrieval are important for animators due to the need to search for motions in the database which can be blended and concatenated. Most of the previous researches of human motion indexing and retrieval compute the Euclidean distance of joint angles or joint positions. Such approaches are difficult to apply for cases in which multiple characters are closely interacting with each other, as the relationships of the characters are not encoded in the representation. In this research, we propose a topology-based approach to index the motions of two human characters in close contact. We compute and encode how the two bodies are tangled based on the concept of rational tangles. The encoded relationships, which we define as TangleList, are used to determine the similarity of the pairs of postures. Using our method, we can index and retrieve motions such as one person piggy-backing another, one person assisting another in walking, and two persons dancing / wrestling. Our method is useful to manage a motion database of multiple characters. We can also produce motion graph structures of two characters closely interacting with each other by interpolating and concatenating topologically similar postures and motion clips, which are applicable to 3D computer games and computer animation.

Using neural networks for learning motion controllers from motion capture data is becoming popular due to the natural and smooth motions they can produce, the wide range of movements they can learn and their compactness once they are trained. Despite these advantages, these systems require large amounts of motion capture data for each new character or style of motion to be generated, and systems have to undergo lengthy retraining, and often reengineering, to get acceptable results. This can make the use of these systems impractical for animators and designers and solving this issue is an open and rather unexplored problem in computer graphics. In this paper we propose a transfer learning approach for adapting a learned neural network to characters that move in different styles from those on which the original neural network is trained. Given a pretrained character controller in the form of a Phase‐Functioned Neural Network for locomotion, our system can quickly adapt the locomotion to novel styles using only a short motion clip as an example. We introduce a canonical polyadic tensor decomposition to reduce the amount of parameters required for learning from each new style, which both reduces the memory burden at runtime and facilitates learning from smaller quantities of data. We show that our system is suitable for learning stylized motions with few clips of motion data and synthesizing smooth motions in real‐time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for the classification and retrieval of interactions between human characters and objects. We propose to use the interaction bisector surface (IBS) between the body and the object as a feature of the interaction. We define a multi-resolution representation of the body structure, and compute a correspondence matrix hierarchy that describes which parts of the character's skeleton take part in the composition of the IBS and how much they contribute to the interaction. Key-frames of the interactions are extracted based on the evolution of the IBS and used to align the query interaction with the interaction in the database. Through the experimental results, we show that our approach outperforms existing techniques in motion classification and retrieval, which implies that the contextual information plays a significant role for scene and interaction description. Our method also shows better performance than other techniques that use features based on the spatial relations between the body parts, or the body parts and the object. Our method can be applied for character motion synthesis and robot motion planning. [ABSTRACT FROM AUTHOR]