让多人个性化图像生成更灵活可控，支持自由布局与缩放。
LayerComposer: Multi-Human Personalized Generation via Layered Canvas
- 用分层画布实现人物位置与大小的直观调整
- 支持多主体生成且计算成本不随人数增加而上升
- 适合需要精细控制多人图像布局的设计师或开发者
现有个性化图像生成方法在视觉质量上表现优异，但在空间布局控制方面缺乏交互性，且难以扩展至多人场景。为解决这一问题，我们提出 LayerComposer，一个交互式且可扩展的多人个性化生成框架。受专业图像编辑软件启发，LayerComposer 提供基于参考的直观人物注入，允许用户直接在分层数字画布上放置和调整多个主体以引导生成。其核心是分层画布表示，每个主体位于独立图层，实现无遮挡的交互式构图。我们进一步引入透明潜在空间剪枝机制，通过解耦计算开销与主体数量提升可扩展性，并采用逐层交叉引用训练策略缓解复制粘贴伪影。大量实验表明，与当前最优方法相比，LayerComposer 在空间控制、构图连贯性和身份保持方面均表现更优。
原文摘要 · Abstract (English)
Despite their impressive visual fidelity, existing personalized image generators lack interactive control over spatial composition and scale poorly to multiple humans. To address these limitations, we present LayerComposer, an interactive and scalable framework for multi-human personalized generation. Inspired by professional image-editing software, LayerComposer provides intuitive reference-based human injection, allowing users to place and resize multiple subjects directly on a layered digital canvas to guide personalized generation. The core of our approach is the layered canvas, a novel representation where each subject is placed on a distinct layer, enabling interactive and occlusion-free composition. We further introduce a transparent latent pruning mechanism that improves scalability by decoupling computational cost from the number of subjects, and a layerwise cross-reference training strategy that mitigates copy-paste artifacts. Extensive experiments demonstrate that LayerComposer achieves superior spatial control, coherent composition, and identity preservation compared to state-of-the-art methods in multi-human personalized image generation.
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