Sangbeom Lim — accepted AI-conference papers

2026

VideoMaMa: Mask-Guided Video Matting via Generative Prior

Sangbeom Lim, Seoung Wug Oh, Jiahui Huang, Heeji Yoon, Seungryong Kim, Joon-Young Lee

CVPR 2026

Generalizing video matting models to real-world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask-to-Matte Model VideoMaMa that converts coarse segmentation masks into pixel accurate alpha mattes, by leveraging pretrained video diffusi

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2025

Multi-Granularity Video Object Segmentation

Sangbeom Lim, Seongchan Kim, Seungjun An, Seokju Cho, Paul Hongsuck Seo, Seungryong Kim

AAAI 2025technical

Current benchmarks for video segmentation are limited to annotating only salient objects (i.e., foreground instances). Despite their impressive architectural designs, previous works trained on these benchmarks have struggled to adapt to realworld scenarios. Thus, developing a new video segmentation…

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