FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
Yang Liu, Cheng Yu, Lei Shang, Yongyi He, Ziheng Wu, Xingjun Wang,, Chao Xu, Haoyu Xie, Weida Wang, Yuze Zhao, Lin Zhu, Chen Cheng, Weitao Chen,, Yuan Yao, Wenmeng Zhou, Jiaqi Xu, Qiang Wang, Yingda Chen, Xuansong Xie,, Baigui Sun

TL;DR
FaceChain is a personalized portrait generation framework that leverages face-related perceptual models to produce truthful, high-quality human portraits from limited input images, improving over existing methods.
Contribution
The paper introduces FaceChain, integrating state-of-the-art face models into image generation to enhance personalization and accuracy in portrait synthesis from few images.
Findings
Improved facial detail accuracy compared to prior methods
Efficient label-tagging and data processing with face models
Enables applications like virtual try-on and talking head
Abstract
Recent advancement in personalized image generation have unveiled the intriguing capability of pre-trained text-to-image models on learning identity information from a collection of portrait images. However, existing solutions are vulnerable in producing truthful details, and usually suffer from several defects such as (i) The generated face exhibit its own unique characteristics, \ie facial shape and facial feature positioning may not resemble key characteristics of the input, and (ii) The synthesized face may contain warped, blurred or corrupted regions. In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to…
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Taxonomy
TopicsFace recognition and analysis · Generative Adversarial Networks and Image Synthesis · AI in cancer detection
