Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Works


MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency


Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting


Experiments
5.1. Pre-training on image data
5.2. Training on video data


Discussion and References Related Works Related Works Related Works MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency MaGGIe 3.1. Efficient Masked Guided Instance Matting 3.1. Efficient Masked Guided Instance Matting 3.2. Feature-Matte Temporal Consistency 3.2. Feature-Matte Temporal Consistency Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting Instance Matting Datasets Instance Matting Datasets 4.1. Image Instance Matting and 4.2. Video Instance Matting 4.1. Image Instance Matting and 4.2. Video Instance Matting Experiments
5.1. Pre-training on image data
5.2. Training on video data Experiments Experiments 5.1. Pre-training on image data 5.1. Pre-training on image data 5.2. Training on video data 5.2. Training on video data Discussion and References Discussion and References Discussion and References Supplementary Material Supplementary Material Architecture details


Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images


Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results Architecture details Architecture details Architecture details Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images Image matting 8.1. Dataset generation and preparation 8.1. Dataset generation and preparation 8.2. Training details 8.2. Training details 8.3. Quantitative details 8.3. Quantitative details 8.4. More qualitative results on natural images 8.4. More qualitative results on natural images Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results Video matting 9.1. Dataset generation 9.1. Dataset generation 9.2. Training details 9.2. Training details 9.3. Quantitative details 9.3. Quantitative details 9.4. More qualitative results 9.4. More qualitative results 2. Related Works There are many ways to categorize matting methods, here we revise previous works based on their primary input types. The brief comparison of others and our MaGGIe is shown in Table 1. Image Matting. Traditional matting methods [4, 24, 25] rely on color sampling to estimate foreground and background, often resulting in noisy outcomes due to limited high-level object features. Advanced deep learningbased methods [9, 11, 31, 37, 46, 47, 54] have significantly improved results by integrating image and trimap inputs or focusing on high-level and detailed feature learning. However, these methods often struggle with trimap inaccuracies and assume single-object scenarios. Recent approaches [5, 6, 22] require only image inputs but face challenges with multiple salient objects. MGM [56] and its extension MGM-in-the-wild [39] introduce binary maskbased matting, addressing multi-salient object issues and reducing trimap dependency. InstMatt [49] further customizes this approach for multi-instance scenarios with a complex refinement algorithm. Our work extends these developments, focusing on efficient, end-to-end instance matting with binary mask guidance. Image matting also benefits from diverse datasets [22, 26, 27, 29, 33, 50, 54], supplemented by background augmentation from sources like BG20K [29] or COCO [35]. Our work also leverages currently available datasets to concretize a robust benchmark for human-masked guided instance matting. Image Matting. Video Matting. Temporal consistency is a key challenge in video matting. Trimap-propagation methods [17, 45, 48] and background knowledge-based approaches like BGMv2 [33] aim to reduce trimap dependency. Recent techniques [28, 32, 34, 53, 57] incorporate ConvGRU, attention memory matching, or transformer-based architectures for temporal feature aggregation. SparseMat [50] uniquely focuses on fusing outputs for consistency. Our approach builds on these foundations, combining feature and output fusion for enhanced temporal consistency in alpha maps. There is a lack of video matting datasets due to the difficulty in data collecting. VideoMatte240K [33] and VM108 [57] focus on composited videos, while CRGNN [52] is the only offering natural videos for human matting. To address the gap in instanceaware video matting datasets, we propose adapting existing public datasets for training and evaluation, particularly for human subjects. Video Matting. Authors:
(1) Chuong Huynh, University of Maryland, College Park (chuonghm@cs.umd.edu);
(2) Seoung Wug Oh, Adobe Research (seoh,jolee@adobe.com);
(3) Abhinav Shrivastava, University of Maryland, College Park (abhinav@cs.umd.edu);
(4) Joon-Young Lee, Adobe Research (jolee@adobe.com). Authors: Authors: (1) Chuong Huynh, University of Maryland, College Park (chuonghm@cs.umd.edu); (2) Seoung Wug Oh, Adobe Research (seoh,jolee@adobe.com); (3) Abhinav Shrivastava, University of Maryland, College Park (abhinav@cs.umd.edu); (4) Joon-Young Lee, Adobe Research (jolee@adobe.com). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

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Evolution of Matting: From Traditional Sampling to MaGGIe's Instance Approach

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