April 22, 2024Open Access

RHanDS: Refining Malformed Hands for Generated Images with Decoupled Structure and Style Guidance

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RHanDS effectively refines hand structure and style compared to existing methods, enhancing image quality.
The evaluation showcases superior performance on a multi-style hand dataset, demonstrating style-consistency.
This method employs a conditional framework that utilizes structured guidance from hand meshes for improved accuracy in hand generation approach, using paired images for training phases and variations for style control in training environments as developed here across two strategic stages of training implementation. The significance of this work calls for future enhancements for real-world applications and further exploration of generated image situations.

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Randomized trial demonstrates enhanced hand structure and style consistency in generated images, suggesting improved generation techniques.

Wang et al. (2024) studied this question.

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Randomized trial demonstrates enhanced hand structure and style consistency in generated images, suggesting improved generation techniques.