Topology-based character motion synthesis

This thesis tackles the problem of automatically synthesizing motions of close-character interactions which appear in animations of wrestling and dancing. Designing such motions is a daunting task even for experienced animators as the close contacts between the characters can easily result in collisions or penetrations of the body segments. The main problem lies in the conventional representation of the character states that is based on the joint angles or the joint positions. As the relationships between the body segments are not encoded in such a representation, the path-planning for valid motions to switch from one posture to another requires intense random sampling and collision detection in the state-space. In order to tackle this problem, we consider to represent the status of the characters using the spatial relationship of the characters. Describing the scene using the spatial relationships can ease users and animators to analyze the scene and synthesize close interactions of characters. We first propose a method to encode the relationship of the body segments by using the Gauss Linking Integral (GLI), which is a value that specifies how much the body segments are winded around each other. We present how it can be applied for content-based retrieval of motion data of close interactions, and also for synthesis of close character interactions. Next, we propose a representation called Interaction Mesh, which is a volumetric mesh composed of points located at the joint position of the characters and vertices of the environment. This raw representation is more general compared to the tangle-based representation as it can describe interactions that do not involve any tangling nor contacts. We describe how it can be applied for motion editing and retargeting of close character interaction while avoiding penetration and pass-throughs of the body segments. The application of our research is not limited to computer animation but also to robotics, where making robots conduct complex tasks such as tangling, wrapping, holding and knotting are essential to let them assist humans for the daily life

Human Motion Retrieval Using Video or Drawn Sketch

The importance of motion retrieval is increasing now a days. The majority of existing motion retrieval labor intensive, there has been a recent paradigm move in the animation industry with an increasing use of pre-recorded movement of animating exclusive figures. An essential need to use motion catch data is an efficient method for listing and accessing movements. I n this work, a novel sketching interface for interpreting the problem is provided. This simple strategy allows the user to determine the necessary movement by drawing several movement swings over a attracted personality, which needs less effort and extends the users expressiveness. To support the real-time interface, a specific development of the movements and the hand-drawn question is needed. Here we are implementing the Conjugate Gradient method for retrieving motion from hand drawn sketch and video. It is an optimization and prominent iterative method. It is fast and uses a small amount of storage

Indexing and Retrieving Motions of Characters in Close Contact

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