从单张图学会部件概念，让AI自由组合新物体。
PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
- 用动态数据合成解决单图训练数据少的问题。
- 通过最大化潜在特征与概念码互信息，实现部件解耦。
- 支持跨类别部件混合，适合可控图像生成任务。
我们提出PartComposer：一种从单张图像中学习部件级概念的框架，使文本到图像扩散模型能够基于有意义的组件生成新物体。现有方法或难以有效学习细粒度概念，或需大量数据输入。我们设计了一种动态数据合成流程，生成多样化的部件组合以应对单样本数据稀缺问题。最重要的是，我们通过概念预测器最大化去噪潜变量与结构化概念码之间的互信息，实现对概念解耦和重新组合的直接调控。该方法在部件解耦和可控组合方面表现优异，在混合同类别或不同类别概念时均优于主体级和部件级基线方法。
原文摘要 · Abstract (English)
We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between denoised latents and structured concept codes via a concept predictor, enabling direct regulation on concept disentanglement and re-composition supervision. Our method achieves strong disentanglement and controllable composition, outperforming subject and part-level baselines when mixing concepts from the same, or different, object categories.
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