# Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priors

arXiv: https://arxiv.org/abs/2501.16147 · HTML: https://mlsanity.com/paper/2501.16147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 28 of 28)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [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)
- [Transparent Image Layer Diffusion using Latent Transparency](https://mlsanity.com/paper/2402.17113/) (2024)
- [Background Matting: The World is Your Green Screen](https://mlsanity.com/paper/2004.00626/) (2020)
- [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)
- [Natural Image Matting via Guided Contextual Attention](https://mlsanity.com/paper/2001.04069/) (2020)
- [Semantic Human Matting](https://mlsanity.com/paper/1809.01354/) (2018)
- [Information-Flow Matting](https://mlsanity.com/paper/1707.05055/) (2017)
- [Deep Automatic Natural Image Matting](https://mlsanity.com/paper/2107.07235/) (2021)
- [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://mlsanity.com/paper/2305.15272/) (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)
- [VMFormer: End-to-End Video Matting with Transformer](https://mlsanity.com/paper/2208.12801/) (2022)
- [TransMatting: Enhancing Transparent Objects Matting with Transformers](https://mlsanity.com/paper/2208.03007/) (2022)
- [OmnimatteRF: Robust Omnimatte with 3D Background Modeling](https://mlsanity.com/paper/2309.07749/) (2023)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Sparse Coding for Alpha Matting](https://mlsanity.com/paper/1604.02898/) (2016)
- [Hierarchical and Progressive Image Matting](https://mlsanity.com/paper/2210.06906/) (2022)
- [EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting](https://mlsanity.com/paper/2308.12831/) (2023)
- [End-to-End Human Instance Matting](https://mlsanity.com/paper/2403.01510/) (2024)
- [Self-supervised Matting-specific Portrait Enhancement and Generation](https://mlsanity.com/paper/2208.06601/) (2022)
- [Patch alignment manifold matting](https://mlsanity.com/paper/1904.07588/) (2019)

## Built on by (most cited first, 1 of 1)

- [DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers](https://mlsanity.com/paper/2505.21541/) (2025)

## Abstract (arXiv)

Learning effective deep portrait matting models requires training data of both high quality and large quantity. Neither quality nor quantity can be easily met for portrait matting, however. Since the most accurate ground-truth portrait mattes are acquired in front of the green screen, it is almost impossible to harvest a large-scale portrait matting dataset in reality. This work shows that one can leverage text prompts and the recent Layer Diffusion model to generate high-quality portrait foregrounds and extract latent portrait mattes. However, the portrait mattes cannot be readily in use due to significant generation artifacts. Inspired by the connectivity priors observed in portrait images, that is, the border of portrait foregrounds always appears connected, a connectivity-aware approach is introduced to refine portrait mattes. Building on this, a large-scale portrait matting dataset is created, termed LD-Portrait-20K, with 20,051 portrait foregrounds and high-quality alpha mattes. Extensive experiments demonstrated the value of the LD-Portrait-20K dataset, with models trained on it significantly outperforming those trained on other datasets. In addition, comparisons with the chroma keying algorithm and an ablation study on dataset capacity further confirmed the effectiveness of the proposed matte creation approach. Further, the dataset also contributes to state-of-the-art video portrait matting, implemented by simple video segmentation and a trimap-based image matting model trained on this dataset.