3D Human Motion Synthesis Based on Convolutional Neural Network

Abstract

Human motion synthesis technology has a very important position in computer animation, and it is widely used in medicine, film and television, motion analysis, games, and other related fields. The synthesis of human motion is the virtual of the action of the characters in the real world, the authenticity of the action, and the natural smoothness is especially important to the user's experience. Due to the complexity of human structure, how to generate a high-quality movement is a challenging task. The data used in this paper are all 3D human motion data in BioVision Hierarchical (BVH) format, which can be captured by optical, inertial, mechanical or other video-based motion capture devices. In this paper, first, a three-layer convolutional neural network was used to output mapping in the hidden unit of the input motion capture data. Then, a one-dimensional convolution auto-encoder was connected; meanwhile, the bone length constraint, position constraint, and trajectory constraint were added. It repaired the non-inertial joints of motion data and removed the motion artifacts. To achieve the synthesis of the two motions, we extracted the style transformation in the motion, added style and content constraints, and finally output the motion. To verify the feasibility of the algorithm, we obtained the animation effect of the synthesized motion by testing the input motion. The experimental results show that the motions synthesized by the proposed algorithm not only look natural smooth in visual effect but also reduce the time consumed by about 42.6% compared with the existing algorithms.