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 6. Discussion Limitation and Future work. Our MaGGIe demonstrates effective performance in human video instance matting with binary mask guidance, yet it also presents opportunities for further research and development. One notable limitation is the reliance on one-hot vector representation for each location in the guidance mask, necessitating that each pixel is distinctly associated with a single instance. This requirement can pose challenges, particularly when integrating instance masks from varied sources, potentially leading to misalignments in certain regions. Additionally, the use of composite training datasets may constrain the model’s ability to generalize effectively to natural, real-world scenarios. While the creation of a comprehensive natural dataset remains a valuable goal, we propose an interim solution: the utilization of segmentation datasets combined with self-supervised or weakly-supervised learning techniques. This approach could enhance the model’s adaptability and performance in more diverse and realistic settings, paving the way for future advancements in the field. Limitation and Future work. Conclusion. Our study contributes to the evolving field of instance matting, with a focus that extends beyond human subjects. By integrating advanced techniques like transformer attention and sparse convolution, MaGGIe shows promising improvements over previous methods in detailed accuracy, temporal consistency, and computational efficiency for both image and video inputs. Additionally, our approach in synthesizing training data and developing a comprehensive benchmarking schema offers a new way to evaluate the robustness and effectiveness of models in instance matting tasks. This work represents a step forward in video instance matting and provides a foundation for future research in this area. Conclusion Acknowledgement. We sincerely appreciate Markus Woodson for the invaluable initial discussions. Additionally, I am deeply thankful to my wife, Quynh Phung, for her meticulous proofreading and feedback. Acknowledgement References [1] Adobe. Adobe premiere. https://www.adobe.com/ products/premiere.html, 2023. 1 [2] Apple. Cutouts object ios 16. https://support. apple.com/en-hk/102460, 2023. 1 [3] Nicolas Ballas, Li Yao, Chris Pal, and Aaron Courville. Delving deeper into convolutional networks for learning video representations. arXiv preprint arXiv:1511.06432, 2015. 4 [4] Arie Berman, Arpag Dadourian, and Paul Vlahos. Method for removing from an image the background surrounding a selected object, 2000. US Patent 6,134,346. 2 [5] Guowei Chen, Yi Liu, Jian Wang, Juncai Peng, Yuying Hao, Lutao Chu, Shiyu Tang, Zewu Wu, Zeyu Chen, Zhiliang Yu, et al. 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In CVPR, 2023. 2, 3, 4, 5, 6, 7, 12, 13, 16, 17, 18, 20 [51] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 30, 2017. 3 [52] Tiantian Wang, Sifei Liu, Yapeng Tian, Kai Li, and MingHsuan Yang. Video matting via consistency-regularized graph neural networks. In ICCV, 2021. 3, 5 [53] Yumeng Wang, Bo Xu, Ziwen Li, Han Huang, Cheng Lu, and Yandong Guo. Video object matting via hierarchical space-time semantic guidance. In WACV, 2023. 2, 3 [54] Ning Xu, Brian Price, Scott Cohen, and Thomas Huang. Deep image matting. In CVPR, 2017. 2 [55] Zongxin Yang, Yunchao Wei, and Yi Yang. Associating objects with transformers for video object segmentation. NeurIPS, 2021. 2, 3, 11 [56] Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, and Alan Yuille. Mask guided matting via progressive refinement network. In CVPR, 2021. 1, 2, 3, 5, 6, 7, 11, 13, 16, 17, 18, 19 [57] Yunke Zhang, Chi Wang, Miaomiao Cui, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Hujun Bao, Qixing Huang, and Weiwei Xu. Attention-guided temporally coherent video object matting. In ACM MM, 2021. 3, 5, 6, 7 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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