The pursuit of controllability as a higher standard of visual content creation has yielded
remarkable progress in customizable image synthesis. However, achieving controllable …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Text-to-image generation has recently witnessed remarkable achievements. We introduce a
text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images …

The development of text-to-video (T2V), ie, generating videos with a given text prompt, has
been significantly advanced in recent years. However, relying solely on text prompts often …

Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse
video generations. Given a set of video clips of the same motion concept, the task of Motion …

Video moment retrieval pursues an efficient and generalized solution to identify the specific
temporal segments within an untrimmed video that correspond to a given language …

Synthesizing images with user-specified subjects has received growing attention due to its
practical applications. Despite the recent success in single subject customization, existing …

Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-
image (T2I) diffusion models. However auxiliary modules have to be trained for each spatial …

Denoising diffusion models have emerged as a powerful tool for various image generation
and editing tasks, facilitating the synthesis of visual content in an unconditional or input …

Recent advancements in subject-driven image generation have led to zero-shot generation
yet precise selection and focus on crucial subject representations remain challenging …