Portrait Image Warping and Inpainting for Identity-Preserving Animation
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Solution Overview
Problem
Existing portrait animation techniques using machine learning models often result in blurry animations with undesired artifacts, reduced sharpness, and fail to preserve the identity of the individual or accurately follow the motion in the driving video.
Innovation Solution
Integrate a residual inpainting module into the machine learning model architecture to preserve original information from the source image, use a local facial region loss during training to enhance facial motion details, and employ a cross-driven training strategy to mitigate appearance leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing portrait animation techniques are used, then the animation generation process is simple, but the output quality is poor with blurriness and artifacts
Solution Approach 1:
The patent divides the image generation process into multiple stages: feature extraction from source and driving images, motion estimation, and residual inpainting. The residual inpainting module specifically processes only the regions requiring modification, segmenting the complex task into manageable parts that improve quality without overwhelming complexity
Solution Approach 2:
The patent introduces an intermediary residual inpainting module between the motion estimation and final image generation. This intermediary component processes the difference between source and driving images, acting as a mediator that refines the animation quality by addressing specific regions needing improvement
2Measurement precision
If existing portrait animation techniques are used, then the processing speed is fast, but the facial motion details are lost and identity is not preserved
Solution Approach 1:
The patent applies local quality enhancement by focusing the residual inpainting module on specific facial regions that require improvement. The local facial region loss function specifically targets facial areas to preserve motion details and identity, applying enhanced processing only where needed rather than uniformly across the entire image
Solution Approach 2:
The patent uses partial action by processing only the residual differences between source and driving images through the residual inpainting module, rather than regenerating the entire image. This partial processing approach preserves computational efficiency while enhancing the specific details that matter for facial animation quality
3Reliability
If existing portrait animation techniques are used, then the model training is straightforward, but appearance leakage occurs and identity is not preserved
Solution Approach 1:
The patent implements feedback mechanisms through the local facial region loss function and cross-driven training strategy. These feedback loops continuously monitor and adjust the generation process to ensure identity preservation and prevent appearance leakage, providing guidance that improves reliability through iterative refinement
Solution Approach 2:
The patent changes training parameters and strategies by introducing the local facial region loss function and cross-driven training approach. These parameter modifications in the training process enable the model to better preserve identity and avoid appearance leakage, transforming the training dynamics to achieve more reliable results
Data Source
AI summary
The present disclosure describes techniques for generating images using a machine learning model. Features are extracted from a source image by a machine learning model. The source image comprises a portrait of a subject. A warp grid is generated based on the source image and a driving image by the machine learning model. The driving image depicts a pose or a visage. The warp grid indicates differences between the source image and the driving image. A warped source image is generated by applying the warp grid to the source image. A mask and a decoded image are generated based on the warp grid and the extracted features. An output image is generated based on the warped source image, the mask, and the decoded image. The output image depicts the subject having the pose or the visage.