Several approaches were proposed that do not require priors about the object. X2Face [40] uses a dense motion field in order to generate the output video via image warping. Similarly to us they employ a reference pose that is used to obtain a canonical representation of the object. In our...
Paper:《First Order Motion Model for Image Animation》翻译与解读,程序员大本营,技术文章内容聚合第一站。
We presented a novel approach for image animation based on keypoints and local affine transformations. Our novel mathematical formulation describes the motion field between two frames and is efficiently computed by deriving a first order Taylor expansion approximation. In this way, motion is described ...
Several approaches were proposed that do not require priors about the object. X2Face [40] uses a dense motion field in order to generate the output video via image warping. Similarly to us they employ a reference pose that is used to obtain a canonical representation of the object. In our...
This repository contains the source code for the paper First Order Motion Model for Image Animation - GitHub - AliaksandrSiarohin/first-order-model: This repository contains the source code for the paper First Order Motion Model for Image Animation
可以看出,first-order 的效果还是很不错的,且该方法基于运动模型推导而出,在形式上也具有良好的可解释性。 基本框架 first-order 的算法框架如下图所示,主要包括三个部分的网络,keyporint detector 检测图像中的关键点,以及每个关键点对应的jaccobian矩阵;dense motion network 基于前面的结果生成最终的transform map...
first-order-model: This repository contains the source code for the paper First Order Motion Model for Image Animationgithub.com/AliaksandrSiarohin/first-order-model 文章将介绍怎么样利用java技术来对视频图像提取,变幻,重新生成一段变化后的视频。这个项目主要目的是给大家提供一个案例供学习参考,让大家...
《First Order Motion Model for Image Animation》翻译与解读 Abstract Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior informat...
首先，first-order motion model的算法框架包括三个主要部分：关键点检测器、每个关键点对应的雅可比矩阵以及密集运动网络。关键点检测器用于检测图像中的关键点，雅可比矩阵则记录了关键点的坐标变换，而密集运动网络则基于前两个结果生成最终的变换映射和遮罩映射。在经过变换映射和遮罩处理后，通过解码器生成...
First Order Motion Model与Monkey-Net一样，其亮点在于能够驱动任意类型运动，无需依赖预知目标的具体信息，如骨骼结构。该方法通过自监督学习机制，有效识别并提取图像中的关键点，实现精准的运动模拟。具体表现如下：在实际应用中，First Order Motion Model通过三部分网络协作完成图像动画生成：关键点检测、...