# LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation

arXiv: https://arxiv.org/abs/1703.01560 · HTML: https://mlsanity.com/paper/1703.01560/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 30 of 36)

- [Synthetic Data from Diffusion Models Improves ImageNet Classification](https://mlsanity.com/paper/2304.08466/) (2023)
- [Counterfactual Generative Networks](https://mlsanity.com/paper/2101.06046/) (2021)
- [GIRAFFE HD: A High-Resolution 3D-aware Generative Model](https://mlsanity.com/paper/2203.14954/) (2022)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing](https://mlsanity.com/paper/2112.02236/) (2021)
- [Unsupervised Layered Image Decomposition into Object Prototypes](https://mlsanity.com/paper/2104.14575/) (2021)
- [Learning Generative Models of Textured 3D Meshes from Real-World Images](https://mlsanity.com/paper/2103.15627/) (2021)
- [Adversarial random forests for density estimation and generative modeling](https://mlsanity.com/paper/2205.09435/) (2022)
- [Unsupervised Object Learning via Common Fate](https://mlsanity.com/paper/2110.06562/) (2021)
- [BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys](https://mlsanity.com/paper/2310.10765/) (2023)
- [Unsupervised Foreground Extraction via Deep Region Competition](https://mlsanity.com/paper/2110.15497/) (2021)
- [Collaging Class-specific GANs for Semantic Image Synthesis](https://mlsanity.com/paper/2110.04281/) (2021)
- [Compositional Transformers for Scene Generation](https://mlsanity.com/paper/2111.08960/) (2021)
- [MOVE: Unsupervised Movable Object Segmentation and Detection](https://mlsanity.com/paper/2210.07920/) (2022)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)
- [SIEDOB: Semantic Image Editing by Disentangling Object and Background](https://mlsanity.com/paper/2303.13062/) (2023)
- [Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field](https://mlsanity.com/paper/2302.01579/) (2023)
- [Hadamard product in deep learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2504.13112/) (2025)
- [Learning Foreground-Background Segmentation from Improved Layered GANs](https://mlsanity.com/paper/2104.00483/) (2021)
- [PaintSeg: Training-free Segmentation via Painting](https://mlsanity.com/paper/2305.19406/) (2023)
- [Unsupervised Image Decomposition with Phase-Correlation Networks](https://mlsanity.com/paper/2110.03473/) (2021)
- [GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes](https://mlsanity.com/paper/2106.13215/) (2021)
- [A Realism Metric for Generated LiDAR Point Clouds](https://mlsanity.com/paper/2208.14958/) (2022)
- [Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks](https://mlsanity.com/paper/2106.13041/) (2021)
- [Generator Born from Classifier](https://mlsanity.com/paper/2312.02470/) (2023)
- [Understanding Deep Generative Models with Generalized Empirical Likelihoods](https://mlsanity.com/paper/2306.09780/) (2023)
- [Multi-Agent Amodal Completion: Direct Synthesis with Fine-Grained Semantic Guidance](https://mlsanity.com/paper/2509.17757/) (2025)
- [FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition](https://mlsanity.com/paper/2107.03166/) (2021)
- [Quantifying point cloud realism through adversarially learned latent representations](https://mlsanity.com/paper/2109.11775/) (2021)

## Abstract (arXiv)

We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed GAN learns to generate image background and foregrounds separately and recursively, and stitch the foregrounds on the background in a contextually relevant manner to produce a complete natural image. For each foreground, the model learns to generate its appearance, shape and pose. The whole model is unsupervised, and is trained in an end-to-end manner with gradient descent methods. The experiments demonstrate that LR-GAN can generate more natural images with objects that are more human recognizable than DCGAN.