Generation Of 3D Character Animation Based On Motion Database

The generation of realistic 3D character animation is a popular and difficult issue in theareas of computer graphics and virtual reality. Motion capture, as an important technology in3D motion generation, can obtain highly-fidelity 3D recordings of the motion of a liveperformer, and thus has become the standard motion generation solution in applications likecomputer game, movie special effects, sports simulation systems, and visualization aids formedical analysis. However, motion capture does not offer an animator free interactive control,but only allow one to play back what has been recorded. This drawback makes the existingmotion data difficult to be reused, especially in interactive applications.This thesis focuses on the problem of how to generate new motion with existing motioncapture database under users' guidance. The contributions of this thesis are as follows:1. A motion control method based on 3D motion database and first-order probabilitytransition model is proposed and implemented.How to describe the spatio-temporal structure underlying the motion database is crucial tothe problem of motion generation based on motion database. In this thesis, we model motion asa first-order Markov process, that is, the current motion status is totally dependant on theprevious status. In the preprocess phase of motion database, we first measure the similaritybetween motion clips, according to which we then estimate the transition probabilities betweenthem, and finally build the first-order probability transition model of motion database. In theon-line motion control phase, we search the preprocessed motion database, and choose the clipthat has the largest transition probability with the previous one to be the current clip. Byrepeating the search-and-choose process, a most probable motion sequence is found out. Weverify the effectiveness of our method by adopting sport exercise video and a video interface.The result shows that our method can describe the spatio-temporal structure underlying themotion data effectively and promote the correctness in motion database searching.2. To guarantee the reality of generated motion, a motion stitching method based onmultiresolution analysis framework is studied and implemented.Generally speaking, motion clips retrieved from motion database are not continuous witheach other, so a motion stitching method needs to be adopted to guarantee the smoothness andreality of generated motion sequences. There are many interpolation methods could be used toconcatenate motion clips. However, obtaining a robust estimate of velocity from motioncaptured data is difficult because these data usually oscillate to include fine details that maydistinguish the motion capture data from those data generated by keyframing and physicssimulation. The difficulty of motion capture data concatenation lies in two issues: how toguarantee seamless concatenation between clips and how to maintain the fine details of originalmotion. A seamless motion stitch method based on multiresolution analysis framework isadopted in this thesis. We first decompose motion data into a base signal and levels of detailsignal, then interpolate base signal and detail signals respectively, and finally compose thestitched base signal and detail signals into a highly detailed motion signal, that is the stitchedmotion sequence. Experiment shows that the stitched motion sequence has c1 continuousnessand maintains the original motion details faithfully.3. A motion generation platform based on motion database with a video interface isimplemented.Due to the success of motion capture method, highly-fidelity motion data has rapidlybecome popular and commercial available. If we could utilize existing motion capture databaseand generate new motion under the users' guidance, we will be able to reduce the cost ofanimation production greatly and provide a brand new technology solution for interactivevideo game. In this thesis, we implement a motion generation platform based on motiondatabase with a video interface. In the platform, we first reconstruct the 3D poses from videokey frames under the user's guidance and calculate joints angle curves, then search thedatabase to find out the most probable motion sequence that matches the reconstructed jointsangle curves, and finally seamless stitch the motion clips with multiresolution analysis method.We use sport exercise video and relevant motion database to test the effectiveness of theplatform. Experiment shows that the generated motion is visually compatible with videocontents.