Amodal perception enables humans to perceive entire objects even when parts are occluded, a remarkable cognitive skill that artificial intelligence struggles to replicate. While substantial advancements have been made in image amodal completion, video amodal completion remains underexplored despite its high potential for real-world applications in video editing and analysis. In response, we propose a video amodal completion framework to explore this potential direction. Our contributions include (i) a synthetic dataset for video amodal completion with text description for the object of interest. The dataset captures a variety of object types, textures, motions, and scenarios to support zero-shot transferring on natural videos. (ii) A diffusion-based text-guided video amodal completion framework enhanced with a motion continuity module to ensure temporal consistency across frames. (iii) Zero-shot inference for long video, inspired by temporal diffusion techniques to effectively manage long video sequences while improving inference accuracy and maintaining coherent amodal completions. Experimental results shows the efficacy of our approach in handling video amodal completion, opening potential capabilities for advanced video editing and analysis with amodal completion.
