Intelligent Group Animation Control Incorporating Individual Differences: An Algorithm for Realistic and Diverse Behaviours

This paper presents an intelligent control algorithm for group animation behaviour that integrates individual characteristic differences. By employing spatial region reconstruction, multi-scene differential individual animation behaviour parameter optimization, and grid chunking region matching, the algorithm divides individual autonomous movement scenes. A local obstacle avoidance mechanism is constructed through multi-scene differential individual perception, and a decision function for an anticollision strategy is established. The cuckoo algorithm is applied to extract high-frequency feature vectors from group animation behaviour data, and the group animation behaviour control algorithm is designed through interactive rendering of group autonomous motion scenes. Experimental results demonstrate that the proposed algorithm converges rapidly, exhibits better animation character behaviour coherence, and achieves a global optimal behaviour solution. It accurately guides group behaviour, enhancing the realism and diversity of group animation. This work contributes to the field of computer animation by enabling the simulation of complex group dynamics while preserving individual differences.