LayerComposer: 通过分层画布实现多人类个性化生成
CanvasComposer: Personalized Group Photo Generation via a Multi-Reference Canvas
AI总结：
LayerComposer通过分层画布实现多人类个性化生成，提供直观的人类注入和高效 scalable 的生成框架。
摘要：
尽管现有个性化图像生成器在视觉保真度方面表现出色，但它们缺乏对空间构图的交互控制，并且在处理多个人物时效果不佳。为了解决这些限制，我们提出了LayerComposer，一种交互式且可扩展的多人类个性化生成框架。受专业图像编辑软件的启发，LayerComposer提供直观的基于参考的人类注入功能，允许用户直接在分层数字画布上放置和调整多个主体，以指导个性化生成。我们的核心方法是分层画布，一种新的表示方法，其中每个主体放置在单独的层上，从而实现交互式且无遮挡的构图。我们进一步引入了一种透明潜在剪枝机制，通过将计算成本与主体数量解耦来提高可扩展性，并引入了一种层间交叉参考训练策略，以减轻复制粘贴伪影。广泛的实验表明，与现有最先进方法相比，LayerComposer在多人类个性化图像生成中实现了更优越的空间控制、一致的构图和身份保持。
英文摘要：
Existing personalized image generators still struggle to preserve multiple reference identities in natural and coherent multi-human generations. To address these limitations, we present CanvasComposer, an interactive framework for personalized group photo generation. Inspired by professional image-editing software, CanvasComposer allows users to place reference subjects on a shared canvas, where each subject keeps its own RGBA cutout of the input. This multi-reference canvas preserves reference content under overlap while providing an intuitive interface for organizing multiple identities; the subjects remain separate elements on the input canvas, and the model outputs a single personalized and harmonized image. To keep this representation efficient, transparent latent pruning retains only tokens from each subject's non-transparent region, and cross-reference training mitigates copy-paste artifacts by learning to harmonize references sampled from different images. Extensive experiments demonstrate that CanvasComposer achieves coherent generation and strong identity preservation compared to state-of-the-art methods in multi-human personalized image generation. Project page: https://snap-research.github.io/canvascomposer
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。
