Simulating Natural Motion of Three-Dimensional Animated Characters Using Reinforcement Learning

This theoretical study presents a conceptual investigation of natural motion simulation in three-dimensional animated characters through deep reinforcement learning and physics-based approaches. The primary objective is to develop an analytical framework that systematically elucidates the interaction among intelligent decision-making policies, real dynamic laws, and motion priors, thereby clarifying at a theoretical level- the process of generating believable, stable, and generalizable motion in virtual environments. The central research questions address how imitative and physics-based reward functions can be integrated to simultaneously achieve perceptual naturalness and dynamical coherence, as well as how learned motion policies can generalize to complex and previously unseen conditions. The methodology is entirely theoretical and conceptual, employing analytical-synthetic reasoning, deductive-inductive argumentation, conceptual decomposition of dynamical equations, and principles of policy optimization. Rather than conducting empirical evaluations or numerical implementations, the study focuses on articulating a coherent theoretical structure governing the interaction between policy learning, physical constraints, and generative motion modeling. The conceptual findings suggest that integrating hierarchical reinforcement learning with physics-informed generative models particularly diffusion policies- holds significant potential for bridging the traditional gap between statistical and physics-based approaches to motion synthesis. This framework enriches the theoretical understanding of "naturalness" in motion at the intersection of artificial intelligence and computer graphics and provides a conceptual foundation for the development of advanced systems in digital animation, humanoid robotics, and virtual reality simulations. Finally, future research directions are proposed, emphasizing advanced generative integration, refined biomechanical modeling, and perceptual-aesthetic analyses of motion.

کلیدواژه ها:

Deep Reinforcement Learning ، Natural Motion Simulation ، ۳D Character Animation ، Physics-Based Control ، Diffusion Policy ، Composite Reward Function ، Motion Policy Generalization