# Background Matting: The World is Your Green Screen

arXiv: https://arxiv.org/abs/2004.00626 · HTML: https://mlsanity.com/paper/2004.00626/

## Key facts

- Cited by 105 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 30 of 105)

- [Robust High-Resolution Video Matting with Temporal Guidance](https://mlsanity.com/paper/2108.11515/) (2021)
- [Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition](https://mlsanity.com/paper/2302.11566/) (2023)
- [ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering](https://mlsanity.com/paper/2312.05941/) (2023)
- [NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions](https://mlsanity.com/paper/2212.07626/) (2022)
- [SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting](https://mlsanity.com/paper/2402.18848/) (2024)
- [Human Performance Modeling and Rendering via Neural Animated Mesh](https://mlsanity.com/paper/2209.08468/) (2022)
- [Privacy-Preserving Portrait Matting](https://mlsanity.com/paper/2104.14222/) (2021)
- [Deep Automatic Natural Image Matting](https://mlsanity.com/paper/2107.07235/) (2021)
- [Semantic Image Matting](https://mlsanity.com/paper/2104.08201/) (2021)
- [Omnimatte: Associating Objects and Their Effects in Video](https://mlsanity.com/paper/2105.06993/) (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)
- [Matting Anything](https://mlsanity.com/paper/2306.05399/) (2023)
- [TriHuman : A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis](https://mlsanity.com/paper/2312.05161/) (2023)
- [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)
- [Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition](https://mlsanity.com/paper/2104.05367/) (2021)
- [PP-Matting: High-Accuracy Natural Image Matting](https://mlsanity.com/paper/2204.09433/) (2022)
- [VMFormer: End-to-End Video Matting with Transformer](https://mlsanity.com/paper/2208.12801/) (2022)
- [Decaf: Monocular Deformation Capture for Face and Hand Interactions](https://mlsanity.com/paper/2309.16670/) (2023)
- [MaGGIe: Masked Guided Gradual Human Instance Matting](https://mlsanity.com/paper/2404.16035/) (2024)
- [Convolutional Neural Opacity Radiance Fields](https://mlsanity.com/paper/2104.01772/) (2021)
- [Image2Lego: Customized LEGO Set Generation from Images](https://mlsanity.com/paper/2108.08477/) (2021)
- [IICNet: A Generic Framework for Reversible Image Conversion](https://mlsanity.com/paper/2109.04242/) (2021)
- [Long-Range Feature Propagating for Natural Image Matting](https://mlsanity.com/paper/2109.12252/) (2021)
- [Matting by Generation](https://mlsanity.com/paper/2407.21017/) (2024)
- [OmniPSD: Layered PSD Generation with Diffusion Transformer](https://mlsanity.com/paper/2512.09247/) (2025)
- [One-Trimap Video Matting](https://mlsanity.com/paper/2207.13353/) (2022)
- [Robust Human Matting via Semantic Guidance](https://mlsanity.com/paper/2210.05210/) (2022)

## Abstract (arXiv)

We propose a method for creating a matte -- the per-pixel foreground color and alpha -- of a person by taking photos or videos in an everyday setting with a handheld camera. Most existing matting methods require a green screen background or a manually created trimap to produce a good matte. Automatic, trimap-free methods are appearing, but are not of comparable quality. In our trimap free approach, we ask the user to take an additional photo of the background without the subject at the time of capture. This step requires a small amount of foresight but is far less time-consuming than creating a trimap. We train a deep network with an adversarial loss to predict the matte. We first train a matting network with supervised loss on ground truth data with synthetic composites. To bridge the domain gap to real imagery with no labeling, we train another matting network guided by the first network and by a discriminator that judges the quality of composites. We demonstrate results on a wide variety of photos and videos and show significant improvement over the state of the art.