In this work, we present a novel approach for motion customization in video generation,
addressing the widespread gap in the exploration of motion representation within video …

Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-
video generation, opening up possibilities for video editing based on textual input. However …

We introduce Videoshop, a training-free video editing algorithm for localized semantic edits.
Videoshop allows users to use any editing software, including Photoshop and generative …

Diffusion models excel at high-quality image and video generation. However, a major
drawback is their high latency. A simple yet powerful way to speed them up is by merging …

This paper presents UniVST, a unified framework for localized video style transfer. It
operates without the need for training, offering a distinct advantage over existing methods …

Video Diffusion Transformers (DiTs) have demonstrated significant potential for generating
high-fidelity videos but are computationally intensive. Existing acceleration methods include …

Video diffusion models have achieved impressive realism and controllability but are limited
by high computational demands, restricting their use on mobile devices. This paper …

We introduce OmniCreator, a novel framework that can conduct text-prompted unified
(image+ video) generation as well as editing all in one place. OmniCreator acquires …