Text-to-image diffusion models produce high quality images but do not offer control over
individual instances in the image. We introduce InstanceDiffusion that adds precise instance …

Driven by the scalable diffusion models trained on large-scale datasets text-to-image
synthesis methods have shown compelling results. However these models still fail to …

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 …

Diffusion models have demonstrated great success in the field of text-to-image generation.
However, alleviating the misalignment between the text prompts and images is still …

Text-to-image diffusion models have an unprecedented ability to generate diverse and high-
quality images. However, they often struggle to faithfully capture the intended semantics of …

In this work, we propose a training-free method to inject visual prompts into Multimodal
Large Language Models (MLLMs) through learnable latent variable optimization. We …

Text-to-video (T2V) generation models have advanced significantly, yet their ability to
compose different objects, attributes, actions, and motions into a video remains unexplored …

Recent advancements in large-scale pre-trained text-to-image models have led to
remarkable progress in semantic image synthesis. Nevertheless synthesizing high-quality …

We address the problem of multi-object 3D pose control in image diffusion models. Instead
of conditioning on a sequence of text tokens, we propose to use a set of per-object …

In the rapidly advancing realm of visual generation, diffusion models have revolutionized the
landscape, marking a significant shift in capabilities with their impressive text-guided …