Recent advancements in controllable human image generation have led to
zero-shot generation using structural signals (e.g., pose, depth) or facial
appearance. Yet, generating human images conditioned on multiple parts of human
appearance remains challenging. Addressing this, we introduce Parts2Whole, a
novel framework designed for generating customized portraits from multiple
reference images, including pose images and various aspects of human
appearance. To achieve this, we first develop a semantic-aware appearance
encoder to retain details of different human parts, which processes each image
based on its textual label to a series of multi-scale feature maps rather than
one image token, preserving the image dimension. Second, our framework supports
multi-image conditioned generation through a shared self-attention mechanism
that operates across reference and target features during the diffusion
process. We enhance the vanilla attention mechanism by incorporating mask
information from the reference human images, allowing for the precise selection
of any part. Extensive experiments demonstrate the superiority of our approach
over existing alternatives, offering advanced capabilities for multi-part
controllable human image customization. See our project page at
this https URL

通过多个参考图像生成定制肖像的新框架 Parts2Whole，利用语义感知的外观编码器和共享自注意力机制，在保留细节的同时支持多图像条件生成，实现了对多部分可控人体图像的高级定制能力。