# MaGGIe: Masked Guided Gradual Human Instance Matting

arXiv: https://arxiv.org/abs/2404.16035 · HTML: https://mlsanity.com/paper/2404.16035/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 57 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Grounded Text-to-Image Synthesis with Attention Refocusing](https://mlsanity.com/paper/2306.05427/) (2023)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [Robust High-Resolution Video Matting with Temporal Guidance](https://mlsanity.com/paper/2108.11515/) (2021)
- [MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition](https://mlsanity.com/paper/2011.11961/) (2020)
- [Background Matting: The World is Your Green Screen](https://mlsanity.com/paper/2004.00626/) (2020)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [Bridging Composite and Real: Towards End-to-end Deep Image Matting](https://mlsanity.com/paper/2010.16188/) (2020)
- [Mask Guided Matting via Progressive Refinement Network](https://mlsanity.com/paper/2012.06722/) (2020)
- [Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation](https://mlsanity.com/paper/1909.09725/) (2019)
- [Natural Image Matting via Guided Contextual Attention](https://mlsanity.com/paper/2001.04069/) (2020)
- [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)
- [F, B, Alpha Matting](https://mlsanity.com/paper/2003.07711/) (2020)
- [Deep Video Matting via Spatio-Temporal Alignment and Aggregation](https://mlsanity.com/paper/2104.11208/) (2021)
- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)
- [Video Mask Transfiner for High-Quality Video Instance Segmentation](https://mlsanity.com/paper/2207.14012/) (2022)
- [Attention-guided Temporally Coherent Video Object Matting](https://mlsanity.com/paper/2105.11427/) (2021)

## Built on by (most cited first, 18 of 18)

- [MatAnyone: Stable Video Matting with Consistent Memory Propagation](https://mlsanity.com/paper/2501.14677/) (2025)
- [Composing Object Relations and Attributes for Image-Text Matching](https://mlsanity.com/paper/2406.11820/) (2024)
- [Visual Persona: Foundation Model for Full-Body Human Customization](https://mlsanity.com/paper/2503.15406/) (2025)
- [MatAnyone 2: Scaling Video Matting via a Learned Quality Evaluator](https://mlsanity.com/paper/2512.11782/) (2025)
- [SDMatte: Grafting Diffusion Models for Interactive Matting](https://mlsanity.com/paper/2508.00443/) (2025)
- [VideoMaMa: Mask-Guided Video Matting via Generative Prior](https://mlsanity.com/paper/2601.14255/) (2026)
- [Generative Video Matting](https://mlsanity.com/paper/2508.07905/) (2025)
- [UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors](https://mlsanity.com/paper/2605.00658/) (2026)
- [CineMatte: Background Matting for Virtual Production and Beyond](https://mlsanity.com/paper/2605.18328/) (2026)
- [Object-Aware Video Matting with Cross-Frame Guidance](https://mlsanity.com/paper/2503.01262/) (2025)
- [VRMDiff: Text-Guided Video Referring Matting Generation of Diffusion](https://mlsanity.com/paper/2503.10678/) (2025)
- [Enhancing Image Matting in Real-World Scenes with Mask-Guided Iterative Refinement](https://mlsanity.com/paper/2502.17093/) (2025)
- [MP-Mat: A 3D-and-Instance-Aware Human Matting and Editing Framework with Multiplane Representation](https://mlsanity.com/paper/2504.14606/) (2025)
- [EasyOmnimatte: Taming Pretrained Inpainting Diffusion Models for End-to-End Video Layered Decomposition](https://mlsanity.com/paper/2512.21865/) (2025)
- [Capture Stage Matting: Challenges, Approaches, and Solutions for Offline and Real-Time Processing](https://mlsanity.com/paper/2507.07623/) (2025)
- [Segment and Matte Anything in a Unified Model](https://mlsanity.com/paper/2601.12147/) (2026)
- [SAM2Matting: Generalized Image and Video Matting](https://mlsanity.com/paper/2606.27339/) (2026)
- [Parallax Portrait Matting](https://mlsanity.com/paper/2607.11205/) (2026)

## Abstract (arXiv)

Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios.