Parts2Whole: Generalizable Multi-Part Portrait Customization

Multi-part portrait customization aims to generate realistic human images by assembling specified body parts from multiple reference images, with significant applications in digital human creation. Existing customization methods typically follow two approaches: 1) test-time fine-tuning, which learn concepts effectively but is time-consuming and struggles with multi-part composition; 2) generalizable feed-forward methods, which offer efficiency but lack fine control over appearance specifics. To address these limitations, we present Parts2Whole, a diffusion-based generalizable portrait generator that harmoniously integrates multiple reference parts into high-fidelity human images by our proposed multi-reference mechanism. To adequately characterize each part, we propose a detail-aware appearance encoder, which is initialized and inherits powerful image priors from the pre-trained denoising U-Net, enabling the encoding of detailed information from reference images. The extracted features are incorporated into the denoising U-Net by a shared self-attention mechanism, enhanced by mask information for precise part selection. Additionally, we integrate pose map conditioning to control the target posture of generated portraits, facilitating more flexible customization. Extensive experiments demonstrate the superiority of our approach over existing methods and applicability to related tasks like pose transfer and pose-guided human image generation, showcasing its versatile conditioning. Our project is available at https://huanngzh.github.io/Parts2Whole/