Скачать или смотреть Real-Time Character Rig with Machine Learning Motion Adaptation

This stage in Walking Bread’s technical development focuses on the integration of AI-assisted procedural motion within a real-time rig architecture compatible with Unreal Engine 5, Unity DOTS, and Autodesk Maya. The Bread character rig acts as a hybrid control entity — blending human-authored keyframes with machine-inferred adjustments derived from motion prediction and adaptive learning models.

At the system core lies a reinforcement-driven animation blueprint, where motion data is continuously evaluated through a low-latency neural layer trained on both hand-drawn frame timing and procedural deformation behavior. The AI model interprets acceleration curves, torque values, and environmental cues, dynamically rewriting joint priorities and morph constraints at runtime.

The character’s bread-like anatomy reacts through soft-body simulation nodes, responsive inverse kinematics, and AI-weighted motion blending, maintaining expressive deformation even under physics-induced stress. The training pipeline runs on NVIDIA RTX-based GPU inference, with model exports optimized for TensorRT acceleration inside the engine.

This architecture transforms Walking Bread into a reactive organism of animation, where movement is context-sensitive, fluid, and computationally alive — bridging filmic authorship and interactive emergence in one procedural ecosystem.

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