This work presents AnyDoor a diffusion-based image generator with the power to teleport
target objects to new scenes at user-specified locations with desired shapes. Instead of …

Diffusion models excel at text-to-image generation, especially in subject-driven generation
for personalized images. However, existing methods are inefficient due to the subject …

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited
customization options hinder users from accurately describing desired outputs. For example …

Recent text-to-video diffusion models have achieved impressive progress. In practice, users
often desire the ability to control object motion and camera movement independently for …

Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …

The field of text-conditioned image generation has made unparalleled progress with the
recent advent of latent diffusion models. While revolutionary, as the complexity of given text …

Current subject-driven image generation methods encounter significant challenges in
person-centric image generation. The reason is that they learn the semantic scene and …

Diffusion models generate images with an unprecedented level of quality but how can we
freely rearrange image layouts? Recent works generate controllable scenes via learning …

Text-to-motion diffusion models can generate realistic animations from text prompts, but do
not support fine-grained motion editing controls. In this paper, we present a method for using …

Text-conditional diffusion models are able to generate high-fidelity images with diverse
contents. However, linguistic representations frequently exhibit ambiguous descriptions of …