Generating realistic animated videos from static images is an important area
of research in computer vision. Methods based on physical simulation and motion
prediction have achieved notable advances, but they are often limited to
specific object textures and motion trajectories, failing to exhibit highly
complex environments and physical dynamics. In this paper, we introduce an
open-domain controllable image animation method using motion priors with video
diffusion models. Our method achieves precise control over the direction and
speed of motion in the movable region by extracting the motion field
information from videos and learning moving trajectories and strengths. Current
pretrained video generation models are typically limited to producing very
short videos, typically less than 30 frames. In contrast, we propose an
efficient long-duration video generation method based on noise reschedule
specifically tailored for image animation tasks, facilitating the creation of
videos over 100 frames in length while maintaining consistency in content
scenery and motion coordination. Specifically, we decompose the denoise process
into two distinct phases: the shaping of scene contours and the refining of
motion details. Then we reschedule the noise to control the generated frame
sequences maintaining long-distance noise correlation. We conducted extensive
experiments with 10 baselines, encompassing both commercial tools and academic
methodologies, which demonstrate the superiority of our method. Our project
page: this https URL

在计算机视觉中，从静态图像生成逼真的动画视频是一个重要的研究领域。本文介绍了一种基于运动先验和视频扩散模型的开放领域可控图像动画方法，能够实现对可移动区域的运动方向和速度的精确控制，同时在保持内容、场景和动作协调一致性的同时生成长度超过 100 帧的长时间视频。