This article introduces a two-stage method Diffusion-Vas based on diffusion prior, which is used to solve the occlusion problem in video object segmentation. This method can effectively perform modal video segmentation and content completion, and can accurately track the target and restore its complete form even when the object is completely occluded. By combining the visible mask sequence and the pseudo-depth map, Diffusion-Vas is able to infer the occlusion of the object boundary, and use the conditional generation model to complete the content of the occlusion area, ultimately generating high-fidelity complete modal-free RGB content. Benchmark test results of this method on multiple data sets show that its performance is better than that of many existing methods, especially in complex scenarios.
In the field of video analysis, object persistence understanding is crucial. The innovation of the Diffusion-Vas method lies in its processing of modal objects, breaking through the limitations of traditional methods that only focus on visible objects. Its two-stage design cleverly combines mask generation and content completion, effectively improving the accuracy and robustness of video analysis. In the future, this technology is expected to be widely used in fields such as autonomous driving and surveillance video analysis, providing strong support for more accurate and comprehensive video understanding. Project address: https://diffusion-vas.github.io/