Text-driven image and video diffusion models have recently achieved unprecedented
generation realism. While diffusion models have been successfully applied for image …

Large-scale datasets have played indispensable roles in the recent success of face
generation/editing and significantly facilitated the advances of emerging research fields …

Generating high-quality artistic portrait videos is an important and desirable task in computer
graphics and vision. Although a series of successful portrait image toonification models built …

Abstract Generative Adversarial Networks (GANs) have established themselves as a
prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study …

Existing GAN inversion methods fail to provide codes for reliable reconstruction and flexible
editing simultaneously. This paper presents a transformer-based image inversion and …

The ability of Generative Adversarial Networks to encode rich semantics within their latent
space has been widely adopted for facial image editing. However, replicating their success …

Deepfake is a technology dedicated to creating highly realistic facial images and videos
under specific conditions, which has significant application potential in fields such as …

Although progress has been made for text-to-image synthesis, previous methods fall short of
generalizing to unseen or underrepresented attribute compositions in the input text. Lacking …

StyleGAN is arguably one of the most intriguing and well-studied generative models,
demonstrating impressive performance in image generation, inversion, and manipulation. In …

We present a novel paradigm for high-fidelity face swapping that faithfully preserves the
desired subtle geometry and texture details. We rethink face swapping from the perspective …