3D Human Motion Synthesis Based On Motion Capture Data

With the development of the motion capture technology, getting the human motion data quickly and efficiently has become reality. Motion capture data has been successfully applied to television and video games, etc. Considering the high expenses in motion capture hardware devices, it causes great inconvenience to reuse the motion capture data. As a result, the research on how to reuse the existing data efficiently becomes a hotspot. Motion capture data based human motion synthesis focuses on reusing the existing motion data, for enriching the animation sequences and provide higher efficiency motion synthesis.With studying the key technologies in motion capture data based human motion synthesis a human motion analysis and synthesis system has been developed in this paper.Motion graph for motion synthesis are established based on the similarity computation within motion data sequences. The motion class templates based on motion feature analysis from motion capture data are constructed and implemented in the automatic motion class annotation of unknown motion sequences in database,which provide the semantic description for user interactive control on motion graph based human motion synthesis. Through the shortest path finding on the motion graph,combining the optimal motion translation generated, the new human motion sequences are created according to the user’s requirements.The work mainly focuses on similarity computation of human motion data, creating transition movements and motion graph.1. According to Euclidean distance, the similarity matrix is calculated between motionframes; the Dynamic Time-Warping algorithm is then implemented to find out the shortest path in the similarity matrix and get the similarity measurement between different length motion sequences.2. Overlap method is adopted to define the length of transition movements, using transformation between euler angles and quaternions for spherical interpolation and linear interpolation, transition movement is generated.3. Motion graph is created with the result of similarity analysis and transition movement, which is searched for the shortest path by using Dijkstra algorithm. Finally, the system can generate new human motion sequences which meet the users’ requirement about the mexisting motion capture data.