Video generation has witnessed significant advancements yet evaluating these models
remains a challenge. A comprehensive evaluation benchmark for video generation is …

In this paper we uncover the untapped potential of diffusion U-Net which serves as a" free
lunch" that substantially improves the generation quality on the fly. We initially investigate …

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a
pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task …

Text-driven video generation witnesses rapid progress. However merely using text prompts
is not enough to depict the desired subject appearance that accurately aligns with users' …

Diffusion models gain increasing popularity for their generative capabilities. Recently, there
have been surging needs to generate customized images by inverting diffusion models from …

This work presents FaceComposer, a unified generative model that accomplishes a variety
of facial content creation tasks, including text-conditioned face synthesis, text-guided face …

Text-to-image generative models have enabled high-resolution image synthesis across
different domains, but require users to specify the content they wish to generate. In this …

DiffusionAvatars synthesizes a high-fidelity 3D head avatar of a person offering intuitive
control over both pose and expression. We propose a diffusion-based neural renderer that …

This paper unravels the potential of sketches for diffusion models addressing the deceptive
promise of direct sketch control in generative AI. We importantly democratise the process …

In human-centric content generation the pre-trained text-to-image models struggle to
produce user-wanted portrait images which retain the identity of individuals while exhibiting …