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CariGAN: Caricature generation through weakly paired adversarial learning
Wenbin Li1, Wei Xiong2, Haofu Liao2
1State Key Laboratory for Novel Software Technology, Nanjing University, China.
Summary
CariGAN generates diverse and plausible caricatures using weakly paired image-to-image translation. This novel approach utilizes facial landmarks and an image fusion mechanism for improved facial exaggeration and detail.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Caricature generation traditionally relies on geometric transformations, limiting diversity and richness.
- Generative Adversarial Networks (GANs) offer potential for image-to-image translation but struggle with caricature's distribution variance.
- Existing methods often require pixel-wise paired data, restricting their applicability.
Purpose of the Study:
- To develop a novel approach for generating plausible and diverse caricatures.
- To address limitations of conventional methods, including lack of diversity and reliance on paired data.
- To propose a robust model for weakly paired image-to-image translation in caricature generation.
Main Methods:
- Modeled caricature generation as a weakly paired image-to-image translation task.
- Introduced CariGAN, incorporating manually annotated caricature facial landmarks for constrained exaggeration.
- Implemented an image fusion mechanism for enhanced focus on key facial regions and proposed a diversity loss.
Main Results:
- CariGAN successfully generates visually plausible caricatures with significant exaggeration.
- The model demonstrates enhanced diversity in generated caricatures compared to state-of-the-art methods.
- Experiments on the large-scale "WebCaricature" dataset validate the effectiveness of the proposed approach.
Conclusions:
- CariGAN effectively addresses the challenges of caricature generation, offering improved diversity and plausibility.
- The use of weakly paired data and facial landmarks represents a significant advancement in image-to-image translation for this task.
- The proposed methods provide a robust framework for generating high-quality, diverse caricatures.
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