Progressive Photorealistic Simplification

TL;DR

This paper introduces a progressive, semantic-based image simplification method that preserves photorealism by iteratively removing and inpainting scene elements, leveraging vision-language models and learned verification.

Contribution

It presents a novel framework combining semantic understanding and generative editing for photorealistic image simplification, including a distillation into a video prediction model.

Findings

Produces high-quality, photorealistic simplification trajectories

Enables applications like decluttering and semantic layer decomposition

Offers a more structured alternative to traditional abstraction methods

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.