# POBEVM: Real-time Video Matting via Progressively Optimize the Target Body and Edge

arXiv: https://arxiv.org/abs/2402.09731 · HTML: https://mlsanity.com/paper/2402.09731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Camouflaged Object Segmentation with Distraction Mining](https://mlsanity.com/paper/2104.10475/) (2021)
- [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)
- [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)
- [Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation](https://mlsanity.com/paper/1909.09725/) (2019)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [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)
- [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)
- [Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation](https://mlsanity.com/paper/2201.06889/) (2022)
- [VMFormer: End-to-End Video Matting with Transformer](https://mlsanity.com/paper/2208.12801/) (2022)
- [Trimap-guided Feature Mining and Fusion Network for Natural Image Matting](https://mlsanity.com/paper/2112.00510/) (2021)
- [Improving Deep Image Matting via Local Smoothness Assumption](https://mlsanity.com/paper/2112.13809/) (2021)
- [Adaptive Background Matting Using Background Matching](https://mlsanity.com/paper/2203.05193/) (2022)
- [SGM-Net: Semantic Guided Matting Net](https://mlsanity.com/paper/2208.07496/) (2022)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) based approaches have achieved great performance in video matting. Many of these methods can produce accurate alpha estimation for the target body but typically yield fuzzy or incorrect target edges. This is usually caused by the following reasons: 1) The current methods always treat the target body and edge indiscriminately; 2) Target body dominates the whole target with only a tiny proportion target edge. For the first problem, we propose a CNN-based module that separately optimizes the matting target body and edge (SOBE). And on this basis, we introduce a real-time, trimap-free video matting method via progressively optimizing the matting target body and edge (POBEVM) that is much lighter than previous approaches and achieves significant improvements in the predicted target edge. For the second problem, we propose an Edge-L1-Loss (ELL) function that enforces our network on the matting target edge. Experiments demonstrate our method outperforms prior trimap-free matting methods on both Distinctions-646 (D646) and VideoMatte240K(VM) dataset, especially in edge optimization.