提及层分解
Referring Layer Decomposition
AI总结：
提及层分解任务通过预测RGBA层实现精确的视觉内容控制，结合大规模数据集和评估协议，提升图像编辑和生成的可控性与质量。
摘要：
精确、具有对象意识的视觉内容控制对于高级图像编辑和组合生成至关重要。然而，大多数现有方法整体上操作整个图像，限制了隔离和操控单个场景元素的能力。相反，分层表示，其中场景被明确地分为对象、环境背景和视觉效果，提供了一个更直观和结构化的框架来解释和编辑视觉内容。为了弥合这一差距并实现组合理解和可控编辑，我们引入了提及层分解（RLD）任务，该任务从单个RGB图像中预测完整的RGBA层，基于灵活的用户提示，例如空间输入（例如点、框、掩码）、自然语言描述或其组合。核心是RefLade，一个包含111万张图像-层-提示三元组的大型数据集，由我们的可扩展数据引擎生成，以及1万张手工整理的高质量层。结合基于感知的、与人类偏好对齐的自动评估协议，RefLade将RLD确立为一个明确定义和可基准化的研究任务。在此基础上，我们提出了RefLayer，一个用于提示条件层分解的简单基线，实现了高视觉保真度和语义对齐。广泛的实验显示，我们的方法能够实现有效的训练、可靠的评估和高质量的图像分解，同时表现出强大的零样本泛化能力。
英文摘要：
Precise, object-aware control over visual content is essential for advanced image editing and compositional generation. Yet, most existing approaches operate on entire images holistically, limiting the ability to isolate and manipulate individual scene elements. In contrast, layered representations, where scenes are explicitly separated into objects, environmental context, and visual effects, provide a more intuitive and structured framework for interpreting and editing visual content. To bridge this gap and enable both compositional understanding and controllable editing, we introduce the Referring Layer Decomposition (RLD) task, which predicts complete RGBA layers from a single RGB image, conditioned on flexible user prompts, such as spatial inputs (e.g., points, boxes, masks), natural language descriptions, or combinations thereof. At the core is the RefLade, a large-scale dataset comprising 1.11M image-layer-prompt triplets produced by our scalable data engine, along with 100K manually curated, high-fidelity layers. Coupled with a perceptually grounded, human-preference-aligned automatic evaluation protocol, RefLade establishes RLD as a well-defined and benchmarkable research task. Building on this foundation, we present RefLayer, a simple baseline designed for prompt-conditioned layer decomposition, achieving high visual fidelity and semantic alignment. Extensive experiments show our approach enables effective training, reliable evaluation, and high-quality image decomposition, while exhibiting strong zero-shot generalization capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
