Portrait Animation Model With ControlNet for Identity-Preserving Motion
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Solution Overview
Problem
Existing machine learning models for portrait animation suffer from accuracy issues due to dependence on third-party detectors, motion ambiguity, and identity drift during cross-identity animation, particularly when capturing nuanced facial expressions and head poses.
Innovation Solution
A machine learning model employing a cross-identity training scheme and an auxiliary ControlNet to derive identity-disentangled motion, using pre-trained portrait reenactment networks to generate control images, and local control images to enhance attention to critical facial regions, mitigating appearance leakage and ensuring accurate animation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If third-party detectors are used for portrait animation, then device complexity is reduced, but measurement precision and reliability deteriorate due to motion ambiguity and identity drift
Solution Approach 1:
The patent introduces ControlNet as an intermediary module that processes driving images to extract motion information (poses and expressions) and guides the generation process. This intermediary structure replaces direct dependency on third-party detectors while maintaining the benefit of simplified architecture, thereby improving measurement precision without significantly increasing device complexity.
Solution Approach 2:
The model is segmented into distinct functional components: a generator for creating animated portraits, a ControlNet module for motion extraction, and identity preservation mechanisms. This segmentation allows each component to specialize in specific tasks, improving overall precision while keeping the overall system complexity manageable through modular design.
2Adaptability or versatility
If cross-identity animation is performed, then adaptability is improved, but reliability deteriorates due to identity drift
Solution Approach 1:
The patent employs parameter changes by adjusting the weighting and conditioning strength of identity features during the generation process. By dynamically controlling the influence of source identity features versus driving style features, the model achieves reliable identity preservation while maintaining adaptability for cross-identity animation and style transfer.
Solution Approach 2:
The model incorporates feedback mechanisms through identity loss functions and attention mechanisms that continuously monitor and preserve source identity characteristics during generation. This feedback ensures that even when performing cross-identity animation with diverse styles, the original identity features are reliably maintained without drift.
3Manufacturing precision
If attention is focused on critical facial regions, then manufacturing precision is improved, but device complexity increases due to auxiliary ControlNet
Solution Approach 1:
The patent applies local quality by using ControlNet to provide region-specific guidance for critical facial areas such as eyes, mouth, and eyebrows. Different parts of the facial image receive differentiated attention and control, improving manufacturing precision for facial expressions while the auxiliary ControlNet structure is justified by its targeted local improvement rather than global complexity increase.
Data Source
AI summary
The present disclosure describes techniques for generating images using a machine learning model. A source image and a driving image are received. The source image comprises a portrait of a first subject. The driving image comprises a second subject and depicts a pose or a visage. Appearance features of the first subject are extracted from the source image by a first sub-model of the machine learning model. A masked image is generated based on the driving image. The masked image comprises a mouth region and/or eye regions in the driving image. The pose or the visage is derived based on the driving image and the masked images by a second sub-model of the machine learning model. An image is generated by the machine learning model. The image preserves the appearance features of the first subject and follows the pose or the visage depicted in the driving image.