标题： 从衣橱到画布：衣橱多联画LoRA用于局部可控的人像生成 显示英文标题

标题： From Wardrobe to Canvas: Wardrobe Polyptych LoRA for Part-level Controllable Human Image Generation

摘要： 最近的扩散模型通过学习特定主题实现个性化，使学习到的属性能够整合到生成的图像中。 然而，由于需要精确且一致的属性保留（例如身份、服装细节），个性化的人像生成仍然具有挑战性。 现有的以主题驱动的图像生成方法通常需要（1）在推理时使用少量图像对每个新主题进行微调，或者（2）大规模数据集训练以实现泛化。 这两种方法在计算上都很昂贵，对于实时应用不切实际。 为了解决这些限制，我们提出了Wardrobe Polyptych LoRA，一种用于个性化人像生成的新颖部分级可控模型。 通过仅训练LoRA层，我们的方法在推理时消除了计算负担，同时确保了未见过的主题的高质量合成。 我们的关键思想是将生成条件化为主题的衣橱，并利用空间参考来减少信息丢失，从而提高保真度和一致性。 此外，我们引入了一种选择性主题区域损失，这鼓励模型在训练期间忽略一些参考图像。 我们的损失确保生成的图像更好地与文本提示对齐，同时保持主题完整性。 值得注意的是，我们的Wardrobe Polyptych LoRA在推理阶段不需要额外参数，并使用仅在少量训练样本上训练的单一模型进行生成。 我们构建了一个新的数据集和基准，专门针对个性化人像生成。 大量实验表明，我们的方法在保真度和一致性方面显著优于现有技术，实现了真实且身份保留的全身合成。 显示英文摘要

摘要： Recent diffusion models achieve personalization by learning specific subjects, allowing learned attributes to be integrated into generated images. However, personalized human image generation remains challenging due to the need for precise and consistent attribute preservation (e.g., identity, clothing details). Existing subject-driven image generation methods often require either (1) inference-time fine-tuning with few images for each new subject or (2) large-scale dataset training for generalization. Both approaches are computationally expensive and impractical for real-time applications. To address these limitations, we present Wardrobe Polyptych LoRA, a novel part-level controllable model for personalized human image generation. By training only LoRA layers, our method removes the computational burden at inference while ensuring high-fidelity synthesis of unseen subjects. Our key idea is to condition the generation on the subject's wardrobe and leverage spatial references to reduce information loss, thereby improving fidelity and consistency. Additionally, we introduce a selective subject region loss, which encourages the model to disregard some of reference images during training. Our loss ensures that generated images better align with text prompts while maintaining subject integrity. Notably, our Wardrobe Polyptych LoRA requires no additional parameters at the inference stage and performs generation using a single model trained on a few training samples. We construct a new dataset and benchmark tailored for personalized human image generation. Extensive experiments show that our approach significantly outperforms existing techniques in fidelity and consistency, enabling realistic and identity-preserving full-body synthesis.