[2603.28333] Integrating Multimodal Large Language Model Knowledge into Amodal Completion

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28333 (cs) [Submitted on 30 Mar 2026] Title:Integrating Multimodal Large Language Model Knowledge into Amodal Completion Authors:Heecheol Yun, Eunho Yang View a PDF of the paper titled Integrating Multimodal Large Language Model Knowledge into Amodal Completion, by Heecheol Yun and 1 other authors View PDF HTML (experimental) Abstract:With the widespread adoption of autonomous vehicles and robotics, amodal completion, which reconstructs the occluded parts of people and objects in an image, has become increasingly crucial. Just as humans infer hidden regions based on prior experience and common sense, this task inherently requires physical knowledge about real-world entities. However, existing approaches either depend solely on the image generation ability of visual generative models, which lack such knowledge, or leverage it only during the segmentation stage, preventing it from explicitly guiding the completion process. To address this, we propose AmodalCG, a novel framework that harnesses the real-world knowledge of Multimodal Large Language Models (MLLMs) to guide amodal completion. Our framework first assesses the extent of occlusion to selectively invoke MLLM guidance only when the target object is heavily occluded. If guidance is required, the framework further incorporates MLLMs to reason about both the (1) extent and (2) content of the missing regions. Finally, a visual generative model integrate...