Progressive Photorealistic Simplification

• URL: http://arxiv.org/abs/2605.10409v1
• Date: Mon, 11 May 2026 11:47:44 GMT
• Title: Progressive Photorealistic Simplification
• Abstract summary: We introduce progressive semantic image simplification, a framework that iteratively reduces scene complexity by removing and inpainting elements.<n>Our method combines semantic understanding with generative editing, leveraging Vision-Language Models (VLMs) to identify and prioritize elements for removal.<n>To improve efficiency, we distill this process into an image-to-video generation model that directly predicts coherent simplification sequences from a single input image.
• Score: 35.59806534693362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or paintings. While effective at reducing visual complexity, such approaches typically sacrifice photographic realism. In this work, we explore a complementary direction: simplifying images while preserving their photorealistic appearance. We introduce progressive semantic image simplification, a framework that iteratively reduces scene complexity by removing and inpainting elements in a controlled manner. At each step, the resulting image remains a plausible natural photograph. Our method combines semantic understanding with generative editing, leveraging Vision-Language Models (VLMs) to identify and prioritize elements for removal, and a learned verifier to ensure photorealism and coherence throughout the process. This is implemented via an iterative Select-Remove-Verify pipeline that produces high-quality simplification trajectories. To improve efficiency, we further distill this process into an image-to-video generation model that directly predicts coherent simplification sequences from a single input image. Beyond generating cleaner and more focused compositions, our approach enables applications such as content-aware decluttering, semantic layer decomposition, and interactive editing. More broadly, our work suggests that simplification through structured content removal can serve as a practical mechanism for guiding visual interpretation within the photorealistic domain, complementing traditional abstraction methods.

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