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Controllable Longer Image Animation with Diffusion Models

2024-05-27 16:08:00
Qiang Wang, Minghua Liu, Junjun Hu, Fan Jiang, Mu Xu

Abstract

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: \url{this https URL}

Abstract (translated)

生成真实动画视频的计算机视觉领域是一个重要的研究课题。基于物理模拟和运动预测的方法已经取得了显著的进步,但它们通常局限于特定的物体纹理和运动轨迹,无法展示高度复杂的环境和物理 dynamics。在本文中,我们介绍了一种使用运动优先级和视频扩散模型进行开放领域的可控图像动画方法。我们的方法通过从视频中提取运动场信息来精确控制可移动区域的运动方向和速度,并学习运动轨迹和强度。当前的预训练视频生成模型通常只能生产非常短的视频,通常不到30帧。相比之下,我们提出了一种专门针对图像动画任务的噪声恢复方法,从而实现超过100帧的视频生成,同时保持内容场景和运动协调的一致性。具体来说,我们将去噪过程分解为两个 distinct 的阶段:场景轮廓的塑造和运动细节的精细化。然后我们将噪声重新安排以控制生成的帧序列,保持长距离噪声的相关性。我们与10个基准进行了广泛的实验,涵盖了商业工具和学术方法论,证明了我们方法的优势。我们的项目页面:\url{这个https:// URL}

URL

https://arxiv.org/abs/2405.17306

PDF

https://arxiv.org/pdf/2405.17306.pdf


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