Abstract
While diffusion-based methods have shown impressive capabilities in capturing diverse and complex hairstyles, their ability to generate consistent and high-quality multi-view outputs -- crucial for real-world applications such as digital humans and virtual avatars -- remains underexplored. In this paper, we propose Stable-Hair v2, a novel diffusion-based multi-view hair transfer framework. To the best of our knowledge, this is the first work to leverage multi-view diffusion models for robust, high-fidelity, and view-consistent hair transfer across multiple perspectives. We introduce a comprehensive multi-view training data generation pipeline comprising a diffusion-based Bald Converter, a data-augment inpainting model, and a face-finetuned multi-view diffusion model to generate high-quality triplet data, including bald images, reference hairstyles, and view-aligned source-bald pairs. Our multi-view hair transfer model integrates polar-azimuth embeddings for pose conditioning and temporal attention layers to ensure smooth transitions between views. To optimize this model, we design a novel multi-stage training strategy consisting of pose-controllable latent IdentityNet training, hair extractor training, and temporal attention training. Extensive experiments demonstrate that our method accurately transfers detailed and realistic hairstyles to source subjects while achieving seamless and consistent results across views, significantly outperforming existing methods and establishing a new benchmark in multi-view hair transfer. Code is publicly available at this https URL.
Abstract (translated)
尽管基于扩散的方法在捕捉多样且复杂的发型方面表现出令人印象深刻的能力，但它们生成一致且高质量的多视角输出（对于数字人类和虚拟角色等现实世界应用至关重要）的能力仍然未被充分探索。在这篇论文中，我们提出了Stable-Hair v2，这是一种新型的基于扩散的多视角发际线转换框架。据我们所知，这是首次利用多视角扩散模型进行稳健、高保真且视图一致的跨多个视角发型转换的工作。 我们的方法包括一个全面的多视角训练数据生成管道，其中包括基于扩散的光头转换器（Bald Converter）、数据增强式图像修复模型以及经过面部微调的多视角扩散模型，用于生成高质量的三元组数据集，包含光头图像、参考发型和视图对齐的源-光头成对图片。 我们的多视角发际线转换模型整合了极地-方位嵌入以进行姿态条件控制，并使用时间注意力层确保在不同视角之间的平滑过渡。为了优化这一模型，我们设计了一种新颖的多层次训练策略，包括可操控姿势的身份网络（IdentityNet）训练、发型提取器训练以及时间注意力训练。 通过广泛的实验表明，我们的方法能够准确地将详细且真实的发型转移到源对象上，并实现视图间无缝和一致的结果，在多视角发际线转换方面显著优于现有方法并建立了新的基准。代码在以下链接公开：[此链接]（请替换为实际的URL）。
URL
https://arxiv.org/abs/2507.07591
