Detecting Collisions in Graph-Driven Motion Synthesis (original) (raw)
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Many works in the literature have improved the performance of motion graphs for synthesis the humanlike results in limited domains that necessity few constraints like dance, navigation in small game like environments or in games by the gesture of feedback on a snowboard tutorial. The humanlike cannot exist in an environment without interacting with the world surrounding them; the naturalness of the entire motion extremely depends on the animation of the walking character, the chosen path and the interaction motions. Addressing exact position of end-effectors is the main disadvantage of motion graphs which cause less importance expended to the search for motions with no collision in complex environments or manipulating motions. This fact motivates this approach which is the proposition of an hybrid motion graphs taking advantages of motion graphs to synthesis a natural locomotion and overcoming their limitations in synthesis manipulation motions by combined it with an inverse kinematic method for synthesis the upper-body motions.
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Enacting and capturing real motion for all potential scenarios is terribly expensive; hence, there is a great demand to synthetically generate realistic human motion. However, it is a central conceptual challenge in character animation to generate a large sequence of smooth human motion, in a synthetic way.
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Human motion indexing and retrieval is important for animators due to the need to search the databases for motions which can suitably 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.
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