The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

Animating a still image offers an engaging visual experience. Traditional image animation
techniques mainly focus on animating natural scenes with stochastic dynamics (eg clouds …

Motions in a video primarily consist of camera motion, induced by camera movement, and
object motion, resulting from object movement. Accurate control of both camera and object …

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 …

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

We present an approach to modeling an image-space prior on scene motion. Our prior is
learned from a collection of motion trajectories extracted from real video sequences …

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 …

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects
poses a significant challenge in the field of artificial intelligence. Unfortunately current state …

We introduce DragAnything, which utilizes a entity representation to achieve motion control
for any object in controllable video generation. Comparison to existing motion control …

Video synthesis has recently made remarkable strides benefiting from the rapid
development of diffusion models. However, it still encounters challenges in terms of …