# Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

arXiv: https://arxiv.org/abs/1908.00999 · HTML: https://mlsanity.com/paper/1908.00999/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 30 of 34)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023)
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- [Unbalanced Feature Transport for Exemplar-based Image Translation](https://mlsanity.com/paper/2106.10482/) (2021)
- [DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation](https://mlsanity.com/paper/2207.06124/) (2022)
- [Bi-level Feature Alignment for Versatile Image Translation and Manipulation](https://mlsanity.com/paper/2107.03021/) (2021)
- [Cross-View Exocentric to Egocentric Video Synthesis](https://mlsanity.com/paper/2107.03120/) (2021)
- [Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer](https://mlsanity.com/paper/2108.07520/) (2021)
- [Marginal Contrastive Correspondence for Guided Image Generation](https://mlsanity.com/paper/2204.00442/) (2022)
- [HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge](https://mlsanity.com/paper/2110.00119/) (2021)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (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)
- [Pose-driven Attention-guided Image Generation for Person Re-Identification](https://mlsanity.com/paper/2104.13773/) (2021)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Graph-based Generative Face Anonymisation with Pose Preservation](https://mlsanity.com/paper/2112.05496/) (2021)
- [Auto-regressive Image Synthesis with Integrated Quantization](https://mlsanity.com/paper/2207.10776/) (2022)
- [Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis](https://mlsanity.com/paper/2211.06719/) (2022)
- [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)
- [Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06164/) (2022)
- [Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation](https://mlsanity.com/paper/2310.01680/) (2023)
- [Unsupervised High-Resolution Portrait Gaze Correction and Animation](https://mlsanity.com/paper/2207.00256/) (2022)
- [RFGAN: RF-Based Human Synthesis](https://mlsanity.com/paper/2112.03727/) (2021)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)
- [Introducing Explicit Gaze Constraints to Face Swapping](https://mlsanity.com/paper/2305.16138/) (2023)
- [Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation](https://mlsanity.com/paper/2101.00990/) (2021)
- [Human Pose Transfer with Augmented Disentangled Feature Consistency](https://mlsanity.com/paper/2107.10984/) (2021)
- [End-to-End Learning of Multi-category 3D Pose and Shape Estimation](https://mlsanity.com/paper/2112.10196/) (2021)

## Abstract (arXiv)

In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C2GAN) for the task of keypoint-guided image generation. The proposed C2GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an interactive manner. C2GAN contains two different types of generators, i.e., keypoint-oriented generator and image-oriented generator. Both of them are mutually connected in an end-to-end learnable fashion and explicitly form three cycled sub-networks, i.e., one image generation cycle and two keypoint generation cycles. Each cycle not only aims at reconstructing the input domain, and also produces useful output involving in the generation of another cycle. By so doing, the cycles constrain each other implicitly, which provides complementary information from the two different modalities and brings extra supervision across cycles, thus facilitating more robust optimization of the whole network. Extensive experimental results on two publicly available datasets, i.e., Radboud Faces and Market-1501, demonstrate that our approach is effective to generate more photo-realistic images compared with state-of-the-art models.