Optimized Motion Simplification for Crowd Animation
Simulating a huge number of articulate figures in a real-time application is one of the challenging research topics in character animation. Several researchers have tried to improve the performance of animation using the image-based technique such as ‘impostor.’ This method improved the speed of the animation; however, the accuracy, memory, and interactivity problems related to motion remain to be resolved. In this regard, a ‘motion simplification’ framework for an articulate figure is proposed, which not only improves the speed of the animation but also conserves the features of the original motion. First, the motion sequence is analyzed using mean shift clustering for the purpose of extracting key postures and automatically generating the priority of joint reductions. These motion analysis results are directly utilized as the input of the proposed posture optimization. The least square method is applied in order to minimize the error between the original and the simplified posture. Finally, how to apply the proposed simplified motion in a real-time application without decreasing the visual quality of the scene is shown. The experimental result shows that the proposed motion simplification can be successfully applied in a real-time animation system.