Image Generation Model Training for Face, Hand, and Motion Blur Fidelity
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Solution Overview
Problem
Existing image generation models struggle with ensuring the authenticity of local detail quality and motion blur in animation results, particularly in human-centric videos, due to limitations in learning objectives and motion capture devices.
Innovation Solution
A method and apparatus for training an image generation model by using a reference image and target pose information to generate an intermediate image, focusing on specific regions like the face and hand, with additional loss functions to optimize these regions, and adjusting signal-to-noise ratios for improved accuracy and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image generation models use standard learning objectives, then the overall image generation capability is maintained, but the authenticity of local detail quality and motion blur in animation results deteriorates
Solution Approach 1:
The patent applies local quality by introducing region-specific loss functions that treat different parts of the image differently. Specifically, it implements a face loss function for facial regions and a hand loss function for hand regions, allowing each region to be optimized with appropriate metrics. This resolves the contradiction by maintaining high local detail quality in critical regions while preserving overall animation authenticity through region-adaptive training objectives.
Solution Approach 2:
The patent segments the image into multiple regions of interest (face, hand, and other regions) and applies different loss functions to each segment. This segmentation allows the model to independently optimize local detail quality in specific regions without compromising the global animation results, thereby resolving the contradiction between local authenticity and overall reliability.
2Measurement precision
If image generation models focus on overall image quality, then general generation capability is maintained, but accuracy and definition of specific regions deteriorates
Solution Approach 1:
The patent implements local quality by introducing region-specific loss functions that apply different optimization criteria to different parts of the image. The face loss function uses L1 loss for pixel-wise accuracy, while the hand loss function uses a combination of L1 loss and structural similarity. This allows the model to achieve high measurement precision in specific regions without sacrificing overall generation capability, as each region is optimized according to its specific requirements.
3Productivity
If standard training methods are used, then training efficiency is maintained, but motion blur issues in animation results worsen
Solution Approach 1:
The patent applies parameter changes by modifying the loss function parameters to specifically address motion blur. It introduces a hand loss function that combines L1 loss with structural similarity measurement, and adjusts the weighting parameters to emphasize motion-affected regions. This allows the model to accurately capture motion blur effects while maintaining training efficiency through the use of standard optimization algorithms with customized loss parameters.
Data Source
AI summary
According to an embodiment of the disclosure, a method, apparatus, device and storage medium for training an image generation model is provided. The method includes: obtaining a reference image and a target image; providing, to an image generation model, the reference image and pose information corresponding to the target image to generate an intermediate image, the pose information describing a pose of a target object in the target image; determining a first region in the intermediate image corresponding to a predetermined part of the target object; and training the image generation model based at least on a difference between the first region and a second region in the target image corresponding to the predetermined part.