从衣橱到画布：用于部件级可控人体图像生成的衣橱多联画 LoRA
From Wardrobe to Canvas: Wardrobe Polyptych LoRA for Part-level Controllable Human Image Generation
AI总结：
本文提出衣橱多联画 LoRA，通过仅训练 LoRA 层并以衣橱和空间参考为条件，实现无需推理微调的部件级可控个性化人体图像生成，在保真度和一致性上显著优于现有方法。
摘要：
近年来，扩散模型通过学习特定主体实现个性化，使学习到的属性能够被整合到生成图像中。然而，个性化人体图像生成仍然具有挑战性，因为需要精确且一致地保留属性（例如身份、服装细节）。现有的主体驱动图像生成方法通常需要：（1）在推理时针对每个新主体使用少量图像进行微调，或（2）进行大规模数据集训练以实现泛化。这两种方法计算成本高，且不适用于实时应用。为了解决这些局限性，我们提出了衣橱多联画 LoRA（Wardrobe Polyptych LoRA），这是一种用于个性化人体图像生成的新型部件级可控模型。通过仅训练 LoRA 层，我们的方法消除了推理时的计算负担，同时确保对未见主体的高保真合成。我们的核心思想是以主体的衣橱为生成条件，并利用空间参考来减少信息损失，从而提高保真度和一致性。此外，我们引入了一种选择性主体区域损失，该损失鼓励模型在训练过程中忽略部分参考图像。我们的损失确保生成图像更好地与文本提示对齐，同时保持主体完整性。值得注意的是，我们的衣橱多联画 LoRA 在推理阶段不需要额外参数，并使用在少量训练样本上训练的单一模型进行生成。我们构建了一个专为个性化人体图像生成定制的新数据集和基准。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，能够实现逼真且保持身份的整体人体合成。
英文摘要：
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.
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。
