Towards Open-World Text-Guided Face Image Generation and Manipulation

Abstract

The existing text-guided image synthesis methods can only produce limited quality results with at most \mbox{${\text{256}}^2$} resolution and the textual instructions are constrained in a small Corpus. In this work, we propose a unified framework for both face image generation and manipulation that produces diverse and high-quality images with an unprecedented resolution at 1024 from multimodal inputs. More importantly, our method supports open-world scenarios, including both image and text, without any re-training, fine-tuning, or post-processing. To be specific, we propose a brand new paradigm of text-guided image generation and manipulation based on the superior characteristics of a pretrained GAN model. Our proposed paradigm includes two novel strategies. The first strategy is to train a text encoder to obtain latent codes that align with the hierarchically semantic of the aforementioned pretrained GAN model. The second strategy is to directly optimize the latent codes in the latent space of the pretrained GAN model with guidance from a pretrained language model. The latent codes can be randomly sampled from a prior distribution or inverted from a given image, which provides inherent supports for both image generation and manipulation from multi-modal inputs, such as sketches or semantic labels, with textual guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN.

arXiv admin note: substantial text overlap with arXiv:2012.03308

References in corpus (9)

• Learning Transferable Visual Models From Natural Language Supervision
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
• Zero-Shot Text-to-Image Generation
• Single Image Portrait Relighting
• Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
• Multimodal Convolutional Neural Networks for Matching Image and Sentence
• SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

Cited by in corpus (5)

• Blended Diffusion for Text-driven Editing of Natural Images
• A Survey on Deep Generative 3D-aware Image Synthesis
• AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation
• An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
• Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model