AnyFace++: A Unified Framework for Free-Style Text-to-Face Synthesis and Manipulation

Area of Science:

• Computer Vision
• Artificial Intelligence
• Natural Language Processing

Background:

• Describing human faces requires extensive vocabulary, challenging current text-to-image models limited by training data.
• Existing methods struggle with generalization due to restricted sentence templates and vocabulary.

Purpose of the Study:

• Introduce a novel 'free-style' text-to-face generation and manipulation problem.
• Propose AnyFace++, a solution for open-world scenarios with enhanced flexibility and vocabulary range.

Main Methods:

• Utilize the CLIP model for aligned language-vision feature space learning.
• Incorporate a memory module to convert arbitrary text descriptions into regularized latent embeddings.
• Employ a semi-supervised training scheme and new objective functions to enhance generation quality.

Main Results:

• AnyFace++ demonstrates superior capability in synthesizing and manipulating face images from flexible descriptions.
• Achieved higher diversity and improved semantic consistency in generated realistic face images compared to state-of-the-art methods.

Conclusions:

• AnyFace++ significantly advances text-to-face synthesis by overcoming vocabulary and template limitations.
• The proposed method offers a more versatile and effective solution for real-world applications requiring nuanced face generation and manipulation.