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

arXiv: https://arxiv.org/abs/1808.04859 · HTML: https://mlsanity.com/paper/1808.04859/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2018.

## Built on by (most cited first, 27 of 27)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [Unpaired Deep Image Deraining Using Dual Contrastive Learning](https://mlsanity.com/paper/2109.02973/) (2021)
- [Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation](https://mlsanity.com/paper/2410.00464/) (2024)
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023)
- [Cross-View Exocentric to Egocentric Video Synthesis](https://mlsanity.com/paper/2107.03120/) (2021)
- [Hand-Object Interaction Image Generation](https://mlsanity.com/paper/2211.15663/) (2022)
- [Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images](https://mlsanity.com/paper/2102.11262/) (2021)
- [Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes](https://mlsanity.com/paper/2106.10876/) (2021)
- [IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation](https://mlsanity.com/paper/2407.10937/) (2024)
- [AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild](https://mlsanity.com/paper/2407.18034/) (2024)
- [Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis](https://mlsanity.com/paper/2211.06719/) (2022)
- [Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization](https://mlsanity.com/paper/2506.15980/) (2025)
- [Layout-to-Image Translation with Double Pooling Generative Adversarial Networks](https://mlsanity.com/paper/2108.12900/) (2021)
- [Local and Global GANs with Semantic-Aware Upsampling for Image Generation](https://mlsanity.com/paper/2203.00047/) (2022)
- [Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis](https://mlsanity.com/paper/2307.12084/) (2023)
- [FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation](https://mlsanity.com/paper/2412.02690/) (2024)
- [Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask](https://mlsanity.com/paper/2103.02861/) (2021)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)
- [Enhanced Multi-Scale Cross-Attention for Person Image Generation](https://mlsanity.com/paper/2501.08900/) (2025)
- [Semi-supervised Hand Appearance Recovery via Structure Disentanglement and Dual Adversarial Discrimination](https://mlsanity.com/paper/2303.06380/) (2023)
- [SignLLM: Sign Language Production Large Language Models](https://mlsanity.com/paper/2405.10718/) (2024)
- [Cycle Contrastive Adversarial Learning for Unsupervised image Deraining](https://mlsanity.com/paper/2407.11750/) (2024)
- [VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data](https://mlsanity.com/paper/2207.09204/) (2022)
- [PerSign: Personalized Bangladeshi Sign Letters Synthesis](https://mlsanity.com/paper/2209.14591/) (2022)
- [A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance](https://mlsanity.com/paper/2404.09846/) (2024)
- [Mudragen: Geometrically Supervised Generation of Interacting Two-Hand Mudras for Preserving Indian Classical Dance Heritage](https://mlsanity.com/paper/2609.03415/) (2026)

## Abstract (arXiv)

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.