Research On Human Motion Generation And Editing Based On Motion Capture

Synthesizing virtual character motion in virtual environment is one of researchfocuses in computer graphics and computer animation, which can be used widely togame, advert, movie, military affairs and so on. As the complexity of virtual characterskeleton, synthesizing realistic motion in computer is very difficult. Synthesizing amotion segment must take animator lots of time and energy even by dint of computeranimation software. As the development of motion capture technology, it became realityto generate virtual character motion with motion capture data. This method is easy toimplement and can generate vivid motion, but existing motion capture data are notenough to satisfy application in practice. So, it is need to reuse existing motion capturedata to generate new motion.The aim of this thesis is to generate vivid human motion, based on existing humanmotion data, by simple interactive control of user. This can reduce the complexity ofhuman motion generation and reuse existing human motion better. The key technologiesinclude: nonlinear dimensionality reduction of human motion, motion generation andediting, motion extension, motion segmentation and join, motion style editing and so on.The thesis is focused on the above associated issues, employing the knowledge fromstatistic learning, pattern recognition, data mining, computer graphics, optimizationtheory, etc.This dissertation focuses on the reconstruction problem of geometries and texturesfrom multiple wide baseline images，some creative algorithms and methods have beenproposed, and the highlighted ideas and main contributions are described as follows:An automatic segmentation method for motion based on detecting motionfeature change is proposed. The motion captured by motion capture equipment ofteninclude several semantic features and the motion segments which include singlesemantic feature are needed in motion production, so the long motion which includeseveral semantic features must be segmented as motion segments. The semantic featureof motion belongs to the category of subjective cognition, the motion is mapped tolow-dimensional feature subspace to avoid the difficulty to analyse and model semanticfeature of motion directly and the motion geometric feature is extracted in the featuresubspace. The geometric feature corresponds to the high-level semantic feature and itschange can denote the change of the high-level semantic feature. This method analysesthe high-level semantic feature by analyzing the geometric feature to implementautomatic segmentation for motion in the semantic level.A method of human motion nonlinear dimensional reduction and generation isproposed, based on fast adaptive scaled Gaussian process latent variable models.Through statistical learning on motion data, the motion data are mapped from high-dimensional observation space to low-dimensional latent space to implementnonlinear dimensional reduction, and probability distributing of posture space whichmeasures the nature of posture is obtained. The posture which meets constraints and hasmaximal probability can be computed as the solution of inverse kinematics. Thismethod can avoid cockamamie computation and posture distortion existing in traditionalinverse kinematics. Then, two methods of motion generation are proposed, which arethe motion generation based on motion trajectory editing and the motion generationbased on key frame editing. Compared with the SGPLVM, the FASGPLVM has higherconvergence velocity and precision and extends editing range of motion by adaptingmotion editing direction.An automatic transition method for motion based on motion dynamic model isproposed. The hidden Markov model is introduced to model the motion dynamics, inwhich the motion data are mapped to low-dimensional latent space by the fast adaptivescaled Gaussian process latent variable models and the motion danymics is modeledbased on Markov chain in the latent space, to analyse the high-dimensional data in thelow-dimensional space. To solve the two key problems, estimation of the length oftransition motion and generation of pose of transition motion, an estimation method ofthe length of transition motion based on motion velocity and a generation method ofpose of transition motion based on latent trajectory interposition are proposed.A motion retargeting method orienting human limbs is proposed, whichincludes motion retargeting orienting human lower limbs and motion retargetingorienting human upper limbs. For motion retargeting orienting human lower limbs,motions are classified to two classes by needing to constrain the contact position of footand constraint surface or not. A motion retargeting method based on lower limbs vectorfeature fixedness is proposed for the motions which do not need to constrain the contactposition of foot and constraint surface, and a motion retargeting method based on lowerlimbs motion trajectory projection is proposed for the motions which need to constrainthe contact position of foot and constraint surface. For motion retargeting orientinghuman upper limbs, the motion trajectory, including static state constraint, dynamicconstraint or semantic constraint, is retargeted to the target skeleton model. Then theretargeted motion trajectory is used as constraint to solve inverse kinematics for targetskeleton model to implement motion retargeting.A prototype system of human motion generation and editing based on motioncapture is designed, named motion generation and editing system for3D virtual human,in which methods of this thesis are validated. Some parts of this system have beenapplied to correlative projects.