Despite significant advancements in video generation and editing using diffusion models,
achieving accurate and localized video editing remains a substantial challenge …

Segment anything model (SAM) developed by Meta AI Research has recently attracted
significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is …

Filmmaking and animation production often require sophisticated techniques for
coordinating camera transitions and object movements, typically involving labor-intensive …

Motion control is crucial for generating expressive and compelling video content; however,
most existing video generation models rely mainly on text prompts for control, which struggle …

We present a unified controllable video generation approach AnimateAnything that
facilitates precise and consistent video manipulation across various conditions, including …

Methods for image-to-video generation have achieved impressive, photo-realistic quality.
However, adjusting specific elements in generated videos, such as object motion or camera …

Diffusion models have obtained substantial progress in image-to-video generation.
However, in this paper, we find that these models tend to generate videos with less motion …

We propose a robot learning method for communicating, planning, and executing a wide
range of tasks, dubbed This&That. We achieve robot planning for general tasks by …

The intuitive nature of drag-based interaction has led to its growing adoption for controlling
object trajectories in image-to-video synthesis. Still, existing methods that perform dragging …

Recent years have seen substantial progress in diffusion-based controllable video
generation. However, achieving precise control in complex scenarios, including fine-grained …