Learning Trimaps via Clicks for Image Matting

• URL: http://arxiv.org/abs/2404.00335v2
• Date: Sat, 6 Apr 2024 15:25:51 GMT
• Title: Learning Trimaps via Clicks for Image Matting
• Authors: Chenyi Zhang, Yihan Hu, Henghui Ding, Humphrey Shi, Yao Zhao, Yunchao Wei,
• Abstract summary: We introduce Click2Trimap, an interactive model capable of predicting high-quality trimaps and alpha mattes with minimal user click inputs. In the user study, Click2Trimap achieves high-quality trimap and matting predictions in just an average of 5 seconds per image.
• Score: 103.6578944248185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant advancements in image matting, existing models heavily depend on manually-drawn trimaps for accurate results in natural image scenarios. However, the process of obtaining trimaps is time-consuming, lacking user-friendliness and device compatibility. This reliance greatly limits the practical application of all trimap-based matting methods. To address this issue, we introduce Click2Trimap, an interactive model capable of predicting high-quality trimaps and alpha mattes with minimal user click inputs. Through analyzing real users' behavioral logic and characteristics of trimaps, we successfully propose a powerful iterative three-class training strategy and a dedicated simulation function, making Click2Trimap exhibit versatility across various scenarios. Quantitative and qualitative assessments on synthetic and real-world matting datasets demonstrate Click2Trimap's superior performance compared to all existing trimap-free matting methods. Especially, in the user study, Click2Trimap achieves high-quality trimap and matting predictions in just an average of 5 seconds per image, demonstrating its substantial practical value in real-world applications.

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