Lightweight Physics-Based Character for Generating Sensible Postures in Dynamic Environments

Pose change and environmental interaction have been the primary directions of recent research in the field of physics-based characters. To address the problems of a high production threshold, high real-time arithmetic consumption when combined with deep reinforcement learning, and a lack of realism in existing solutions, in this paper, a real-time automatic motion control model is designed for virtual characters that provides posture control through physical response linkages and that can generate smooth and natural interactions with the environment through small amounts of body adjustments. The proposed model consists of a mixture of kinematic controllers and lightweight physical interaction modules. It uses a designed posture correction module to correct the posture of a hybrid model, automatically adjusting the body posture, and it can respond reasonably to the environment when the action generation encounters environmental obstacles. The designed constraint scheme can reduce the tuning of joint parameters while allowing a character to have realistic human-like motion perception. The proposed model is verified by experiments, and the experimental results demonstrate that the proposed virtual human model can be adjusted to achieve the human-like effect using real action data and keyframe animation. Ball bouncing and walking on irregular terrain experiments verify that the proposed virtual character model can interact with the environment effectively in real-time. As the carrier of character animation, the proposed driving framework model can rapidly and easily generate interactive motions based on given input parameters.