# AlphaGAN: Generative adversarial networks for natural image matting

arXiv: https://arxiv.org/abs/1807.10088 · HTML: https://mlsanity.com/paper/1807.10088/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 30 of 50)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Privacy-Preserving Portrait Matting](https://mlsanity.com/paper/2104.14222/) (2021)
- [Semantic Image Matting](https://mlsanity.com/paper/2104.08201/) (2021)
- [MatteFormer: Transformer-Based Image Matting via Prior-Tokens](https://mlsanity.com/paper/2203.15662/) (2022)
- [Deep Video Matting via Spatio-Temporal Alignment and Aggregation](https://mlsanity.com/paper/2104.11208/) (2021)
- [Attention-guided Temporally Coherent Video Object Matting](https://mlsanity.com/paper/2105.11427/) (2021)
- [Rethinking Portrait Matting with Privacy Preserving](https://mlsanity.com/paper/2203.16828/) (2022)
- [Human Instance Matting via Mutual Guidance and Multi-Instance Refinement](https://mlsanity.com/paper/2205.10767/) (2022)
- [PP-Matting: High-Accuracy Natural Image Matting](https://mlsanity.com/paper/2204.09433/) (2022)
- [TransMatting: Enhancing Transparent Objects Matting with Transformers](https://mlsanity.com/paper/2208.03007/) (2022)
- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)
- [Long-Range Feature Propagating for Natural Image Matting](https://mlsanity.com/paper/2109.12252/) (2021)
- [Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2407.05352/) (2024)
- [One-Trimap Video Matting](https://mlsanity.com/paper/2207.13353/) (2022)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Smart Scribbles for Image Mating](https://mlsanity.com/paper/2103.17062/) (2021)
- [Diffusion for Natural Image Matting](https://mlsanity.com/paper/2312.05915/) (2023)
- [Multi-scale Information Assembly for Image Matting](https://mlsanity.com/paper/2101.02391/) (2021)
- [Salient Image Matting](https://mlsanity.com/paper/2103.12337/) (2021)
- [Prior-Induced Information Alignment for Image Matting](https://mlsanity.com/paper/2106.14439/) (2021)
- [Highly Efficient Natural Image Matting](https://mlsanity.com/paper/2110.12748/) (2021)
- [Deep Image Matting with Flexible Guidance Input](https://mlsanity.com/paper/2110.10898/) (2021)
- [Hierarchical and Progressive Image Matting](https://mlsanity.com/paper/2210.06906/) (2022)
- [Adversarially-Guided Portrait Matting](https://mlsanity.com/paper/2305.02981/) (2023)
- [dugMatting: Decomposed-Uncertainty-Guided Matting](https://mlsanity.com/paper/2306.01452/) (2023)
- [Lightweight Portrait Matting via Regional Attention and Refinement](https://mlsanity.com/paper/2311.03770/) (2023)
- [End-to-End Human Instance Matting](https://mlsanity.com/paper/2403.01510/) (2024)
- [Boosting General Trimap-free Matting in the Real-World Image](https://mlsanity.com/paper/2405.17916/) (2024)
- [Alpha Matte Generation from Single Input for Portrait Matting](https://mlsanity.com/paper/2106.03210/) (2021)
- [Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction](https://mlsanity.com/paper/2110.03278/) (2021)

## Abstract (arXiv)

We present the first generative adversarial network (GAN) for natural image matting. Our novel generator network is trained to predict visually appealing alphas with the addition of the adversarial loss from the discriminator that is trained to classify well-composited images. Further, we improve existing encoder-decoder architectures to better deal with the spatial localization issues inherited in convolutional neural networks (CNN) by using dilated convolutions to capture global context information without downscaling feature maps and losing spatial information. We present state-of-the-art results on the alphamatting online benchmark for the gradient error and give comparable results in others. Our method is particularly well suited for fine structures like hair, which is of great importance in practical matting applications, e.g. in film/TV production.