We introduce Motion-I2V, a novel framework for consistent and controllable text-guided
image-to-video generation (I2V). In contrast to previous methods that directly learn the …

Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-
trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As …

In this work, we present MotionBooth, an innovative framework designed for animating
customized subjects with precise control over both object and camera movements. By …

In recent years, diffusion models have made remarkable strides in text-to-video generation,
sparking a quest for enhanced control over video outputs to more accurately reflect user …

Visual object tracking aims to locate a targeted object in a video sequence based on an
initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize …

Recently, camera pose, as a user-friendly and physics-related condition, has been
introduced into text-to-video diffusion model for camera control. However, existing methods …

Following the advancements in text-guided image generation technology exemplified by
Stable Diffusion, video generation is gaining increased attention in the academic …

With advancements in video generative AI models (eg, SORA), creators are increasingly
using these techniques to enhance video previsualization. However, they face challenges …

Object manipulation in images aims to not only edit the object's presentation but also gift
objects with motion. Previous methods encountered challenges in concurrently handling …

In this paper, we introduce a new challenging task called Zero-Shot Controllable Image-to-
Video Animation, where the goal is to animate an image based on motion trajectories …