In-Context Matting
<|reference_start|>In-Context Matting: We introduce in-context matting, a novel task setting of image matting. Given a reference image of a certain foreground and guided priors such as points, scribbles, and masks, in-context matting enables automatic alpha estimation on a batch of target images of the same foreground category, without additional auxiliary input. This setting marries good performance in auxiliary input-based matting and ease of use in automatic matting, which finds a good trade-off between customization and automation. To overcome the key challenge of accurate foreground matching, we introduce IconMatting, an in-context matting model built upon a pre-trained text-to-image diffusion model. Conditioned on inter- and intra-similarity matching, IconMatting can make full use of reference context to generate accurate target alpha mattes. To benchmark the task, we also introduce a novel testing dataset ICM-$57$, covering 57 groups of real-world images. Quantitative and qualitative results on the ICM-57 testing set show that IconMatting rivals the accuracy of trimap-based matting while retaining the automation level akin to automatic matting. Code is available at https://github.com/tiny-smart/in-context-matting<|reference_end|>
@article{guo2024in-context,
    title={In-Context Matting},
    author={He Guo, Zixuan Ye, Zhiguo Cao, Hao Lu},
    journal={arXiv preprint arXiv:2403.15789},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15789},
    primaryClass={cs.CV}
}