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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 1: AAAI-22 Technical Tracks 1

MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition

February 1, 2023

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Authors

Zhanghan Ke

Department of Computer Science, City University of Hong Kong SenseTime Research


Jiayu Sun

Department of Computer Science, City University of Hong Kong


Kaican Li

SenseTime Research


Qiong Yan

SenseTime Research


Rynson W.H. Lau

Department of Computer Science, City University of Hong Kong


DOI:

10.1609/aaai.v36i1.19999


Abstract:

Existing portrait matting methods either require auxiliary inputs that are costly to obtain or involve multiple stages that are computationally expensive, making them less suitable for real-time applications. In this work, we present a light-weight matting objective decomposition network (MODNet) for portrait matting in real-time with a single input image. The key idea behind our efficient design is by optimizing a series of sub-objectives simultaneously via explicit constraints. In addition, MODNet includes two novel techniques for improving model efficiency and robustness. First, an Efficient Atrous Spatial Pyramid Pooling (e-ASPP) module is introduced to fuse multi-scale features for semantic estimation. Second, a self-supervised sub-objectives consistency (SOC) strategy is proposed to adapt MODNet to real-world data to address the domain shift problem common to trimap-free methods. MODNet is easy to be trained in an end-to-end manner. It is much faster than contemporaneous methods and runs at 67 frames per second on a 1080Ti GPU. Experiments show that MODNet outperforms prior trimap-free methods by a large margin on both Adobe Matting Dataset and a carefully designed photographic portrait matting (PPM-100) benchmark proposed by us. Further, MODNet achieves remarkable results on daily photos and videos.

Topics: AAAI

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HOW TO CITE:

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition AAAI 2022, 1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau (2022). MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition. Proceedings of the AAAI Conference on Artificial Intelligence, 1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau. MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau. 2022. MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition. "Proceedings of the AAAI Conference on Artificial Intelligence". 1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau. (2022) "MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition", Proceedings of the AAAI Conference on Artificial Intelligence, p.1140-1147

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau, "MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition", AAAI, p.1140-1147, 2022.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau. "MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau. "MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1140-1147.

Zhanghan Ke||Jiayu Sun||Kaican Li||Qiong Yan||Rynson W.H. Lau. MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition. AAAI[Internet]. 2022[cited 2023]; 1140-1147.


ISSN: 2374-3468


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