# A Layer-Based Sequential Framework for Scene Generation with GANs

arXiv: https://arxiv.org/abs/1902.00671 · HTML: https://mlsanity.com/paper/1902.00671/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Unsupervised Object Learning via Common Fate](https://mlsanity.com/paper/2110.06562/) (2021)
- [GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes](https://mlsanity.com/paper/2106.13215/) (2021)
- [MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs](https://mlsanity.com/paper/2303.02906/) (2023)
- [PI-Trans: Parallel-ConvMLP and Implicit-Transformation Based GAN for Cross-View Image Translation](https://mlsanity.com/paper/2207.04242/) (2022)
- [Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects](https://mlsanity.com/paper/2410.01816/) (2024)
- [Neural Scene Decoration from a Single Photograph](https://mlsanity.com/paper/2108.01806/) (2021)

## Abstract (arXiv)

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.