Real-time performance-driven finger motion synthesis. (June 2017) - Mousas, Christos; Anagnostopoulos, Christos-Nikolaos
Highlights: A real-time finger motion estimation process. The contraction of a hierarchical HMM for predicting the segment phase and the progression of the motion segment itself. The ability of our system to work in real-time. The ability of our system to synthesize perceptual valid motion sequences. Graphical abstract: Abstract: This paper presents a method to estimate and synthesize the motion of a character's fingers in real-time during the performance capture process. For the motion estimation and synthesis process, different motion datasets are used that contain the full-body motion of a character, including the motion of its fingers. The motion datasets have been pre-processed for being efficiently handled in real-time estimation process. During the runtime of the application, the system recognizes the actions of the user's hands and assembles the necessary motion of the character's fingers. By using a hierarchical Hidden Markov Model (HMM), the system learns the phase of the gestures as well as the progress of the motion. To eliminate the searching process of the most probable motion, prior constraints between segment states were assigned manually. During the runtime of our application, by using a forward HMM algorithm, the system synthesizes the necessary motion of a character's fingers in real-time. The presented methodology is evaluated both numerically (system performance and estimation rate) and perceptually. The results show that, even when a reduced number of finger gestures are used, the synthesized motion can be described as perceptually consistent with the ground truth motion data.