From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation

arXiv:2404.15267v1 Announce Type: new
Abstract: 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 …

arxiv cs.cv framework human image image generation reference type