SCAmodal: Reasoning Aware Amodal Completion via Semantic Geometric Dual Guidance

Abstract

Amodal appearance completion in open-world scenarios requires reconstructing the hidden regions of occluded objects, a task that demands both geometric extrapolation and high-level semantic understanding. Conventional methods predominantly rely on geometric priors, often failing to maintain semantic coherence and structural integrity in complex scenes. In this paper, we propose Semantic Critic Amodal (SCAmodal), a novel reasoning-aware framework designed for category-agnostic amodal completion. By integrating the high-level semantic reasoning of Vision-Language Models (VLMs) with the geometric structural priors of diffusion models, SCAmodal effectively bridges the gap between scene understanding and pixel-level synthesis. The core innovation of our framework is an iterative Semantic Critic mechanism that the VLM acts as a closed-loop validator to enforce texture continuity and structural plausibility. Extensive evaluations on the challenging COCO-A and LAION benchmarks demonstrate that SCAmodal significantly outperforms existing methods, achieving superior visual realism, structural preservation, and perceptual consistency in restoring severely occluded objects.