Head Region Digital Representation Editing for Gaze Correction

The patent divides the machine learning processing into two separate algorithms: a first machine learning algorithm that generates comprehensive reference data, and a second machine learning algorithm that processes input images using that reference data. This segmentation allows the system to maintain comprehensive adjustment capabilities while reducing the computational burden on any single processing stage, as each algorithm can be optimized for its specific function rather than requiring one algorithm to handle all adjustment tasks.

The patent extracts only the essential and most relevant features from the reference data generated by the first machine learning algorithm, rather than transmitting or storing all the detailed data. The second machine learning algorithm is trained to work with this extracted subset of information, maintaining adjustment precision by focusing on the most critical parameters while significantly reducing data storage and bandwidth requirements.

The patent performs preliminary processing by generating comprehensive reference data using the first machine learning algorithm before the actual adjustment operation. This pre-computed reference data is then used by the second machine learning algorithm, which can perform rapid adjustments without needing to recalculate complex transformations in real-time. The computationally intensive work is done in advance, allowing fast processing during actual use while maintaining high accuracy.