MatAnyone: Stable Video Matting with Consistent Memory Propagation
Authors: name: Peiqing Yang, name: Shangchen Zhou, name: Jixin Zhao, name: Qingyi Tao, name: Chen Change Loy

Auxiliary-free human video matting methods, which rely solely on input
frames, often struggle with complex or ambiguous backgrounds. To address this,
we propose MatAnyone, a robust framework tailored for target-assigned video
matting. Specifically, building on a memory-based paradigm, we introduce a
consistent memory propagation module via region-adaptive memory fusion, which
adaptively integrates memory from the previous frame. This ensures semantic
stability in core regions while preserving fine-grained details along object
boundaries. For robust training, we present a larger, high-quality, and diverse
dataset for video matting. Additionally, we incorporate a novel training
strategy that efficiently leverages large-scale segmentation data, boosting
matting stability. With this new network design, dataset, and training
strategy, MatAnyone delivers robust and accurate video matting results in
diverse real-world scenarios, outperforming existing methods.