GestureGAN for Hand Gesture-to-Gesture Translation in the Wild (1808.04859v2)

Abstract: Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we propose a novel hand Gesture Generative Adversarial Network (GestureGAN). GestureGAN consists of a single generator $G$ and a discriminator $D$, which takes as input a conditional hand image and a target hand skeleton image. GestureGAN utilizes the hand skeleton information explicitly, and learns the gesture-to-gesture mapping through two novel losses, the color loss and the cycle-consistency loss. The proposed color loss handles the issue of "channel pollution" while back-propagating the gradients. In addition, we present the Fr\'echet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive experiments on two widely used benchmark datasets demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task. Meanwhile, the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier. Our model and code are available at https://github.com/Ha0Tang/GestureGAN.

• The paper introduces a novel GAN framework that maps input hand gestures to target skeleton representations, achieving detailed and accurate translations.
• It employs innovative color and cycle-consistency loss functions to overcome channel pollution and ensure high-quality image synthesis.
• Experimental results on NTU Hand Digit and Creative Senz3D datasets show enhanced image quality and robustness compared to competing models.

GestureGAN for Hand Gesture-to-Gesture Translation in the Wild

The research paper introduces GestureGAN, an innovative approach to facilitate hand gesture-to-gesture translation in unconstrained environments. This task requires sophisticated mapping between input gestures and output gestures, which vary significantly in pose, size, and location. The advent of GestureGAN marks a significant advancement in handling such complex scenarios through the utilization of Generative Adversarial Networks (GANs).

Technical Contributions

1. Network Architecture:

GestureGAN consists of a single generator and discriminator framework that takes a hand gesture image alongside a target hand skeleton image as input. It relies on the explicit use of hand skeleton data to manage the gesture mapping intricacies effectively. This approach strives to learn high-quality mappings with compelling detail.

2. Novel Loss Functions:

To overcome challenges inherent in image-to-image translation, GestureGAN employs two innovative loss functions: - Color Loss: It addresses the prevalent "channel pollution" issue found in many generative models where errors from adjoining channels impact color consistency during gradient backpropagation. This leads to sharper and more realistic generated images. - Cycle-Consistency Loss: Originally inspired by the CycleGAN framework, this loss function enforces consistency between the input and reconstructed images, ensuring more reliable and accurate translations.

3. Fréchet ResNet Distance (FRD):

To evaluate the authenticity of generated images, the paper introduces the FRD, a novel metric anticipated to better align with human judgment than previous metrics, such as the Fréchet Inception Distance (FID). By assessing feature space similarities derived from a pretrained ResNet, FRD provides a more nuanced evaluation of image quality.

Experimental Results and Implications

Through comprehensive experimentation on two benchmark datasets—NTU Hand Digit and Creative Senz3D—GestureGAN demonstrates superior performance, producing high-quality, photo-realistic images. Several key insights and results are noteworthy:

• Enhanced Performance: GestureGAN consistently outperforms competing models across various metrics, including MSE, PSNR, and IS, suggesting its robustness and accuracy in gesture translation.
• Data Augmentation Potential: The quality of GestureGAN's generated images supports its use in augmenting training datasets, significantly enhancing the performance of gesture classification models.
• Robustness to Variability: The model's ability to maintain performance across different gesture sizes and distances further underscores its adaptability and efficacy.

Implications for Future AI Developments

GestureGAN paves the way for more resilient and versatile AI models capable of performing complex multimedia translation tasks in real-time. Its innovative use of domain-specific skeleton information and novel loss functions can inspire future research to tackle other translation tasks with high variability and complexity. Additionally, the development of specialized metrics like FRD illustrates the potential for more precise evaluation techniques in generative model performance analysis.

Conclusion

GestureGAN presents a sophisticated approach to hand gesture-to-gesture translation, incorporating novel methodologies to overcome traditional challenges in generative modeling. The implications of this work reach into domains requiring seamless human-computer interaction, offering valuable insights and exciting opportunities for advancing AI-driven image synthesis and translation tasks.