Learning Trimaps via Clicks for Image Matting
<|reference_start|>Learning Trimaps via Clicks for Image Matting: 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.<|reference_end|>
@article{zhang2024learning,
    title={Learning Trimaps via Clicks for Image Matting},
    author={Chenyi Zhang, Yihan Hu, Henghui Ding, Humphrey Shi, Yao Zhao, Yunchao
  Wei},
    journal={arXiv preprint arXiv:2404.00335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00335},
    primaryClass={cs.CV}
}