Realistic Digital Human Movement via AI Realization Model

The patent creates virtual reference videos by rendering 3D digital human characters with realistic movements, then uses these virtual copies as training data to teach AI models. This copying approach eliminates the need for expensive motion capture equipment while achieving realistic movements through automated AI learning from synthesized reference data.

The patent replaces the mechanical motion capture system (physical sensors, markers, and equipment) with an AI-based system that learns movement patterns from video data. This substitution eliminates complex physical measurement devices while achieving similar or better results through machine learning.

The patent transfers movement patterns from high-quality reference videos to simplified digital human models through AI learning. This allows lightweight models to replicate realistic movements without requiring the computational resources needed for high-detail models, achieving quality-performance balance.

The patent changes the approach from modifying model parameters (muscle structures, joint configurations) to learning movement patterns from video data. This parameter transformation allows simplified models to achieve realistic movements through AI-driven motion transfer rather than complex anatomical modeling.

The patent creates virtual reference videos that serve as error-free training data, copying realistic movement patterns directly into the AI model without physical measurement errors. This eliminates marker attachment errors, tracking inaccuracies, and manual refinement mistakes that plague traditional motion capture pipelines.

The patent introduces AI models as an intermediary between reference videos and target digital humans. This intermediary learns and transfers movement patterns automatically, eliminating manual data refinement steps where errors typically accumulate during marker correction and joint position adjustment.

The patent enables the AI model to automatically learn and refine movement patterns from video data without human intervention. The system serves itself by generating training data, learning patterns, and applying movements autonomously, replacing expensive manual refinement work with automated machine learning processes.

The patent copies natural movement patterns directly from video data into the AI model through automated learning, eliminating the need for professional designers to manually interpret and refine motion capture data. This copying process achieves naturalness automatically through AI pattern recognition.