# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
###### Abstract
The first type of method relies on pre-trained models to extract specific representations of the target object, such as facial landmarks [10; 11], gesture [12] or human keypoints [13]. However, the performance of these methods depends on the labeled data and pre-trained model. The construction of the pre-trained model requires the annotation of the ground-truth data, which requires expensive acquisition. In addition, these pre-trained models do not generally apply to all types of object categories. Another method is unsupervised motion transfer, which does not require real data on the ground. X2 Face [14], proposed by [PERSON] et al., is a self-supervised neural network model that uses another person's face to control the pose and expression of a given face, but the error generated by this method is obvious. [PERSON] et al. proposed Monkey-Net [15], which is the first depth model for image animation of unknowable objects. It extracts target keypoints in the image through a self-supervised keypoint detector and generates a dense heatmap from sparse keypoints. Following this, the input frame, which uses the motion heatmap and appearance information extracted from the input image, is synthesized. However, it is difficult for Monkey-Net to model the appearance transformation of objects near the keypoints, which leads to poor generation quality when the scale of object change is quite large. To support more complex motion, FOMM [16] was proposed to use unsupervised learning keypoints and local affine transformation to simulate complex motion. In applications, the methods of image animation based on pre-trained models have many limitations. Meanwhile, research on the application of image animation without relying on labeled data and pre-trained models has achieved great progress. However, there are still some problems in the current unsupervised methods. For example, sometimes  
Figure 1: Our method animates still source images. the prediction of the optical flow field is not accurate, which will result in incorrect or low-quality generated frames. Sometimes, the predicted keypoints are located on the background instead of on the moving objects. Hence, the displacement deformation between keypoints cannot accurately describe the displacement deformation of the rigid region of the animation object. The ghosting effect (false object shadow) often occurs in the generated frames. Although FOMM is an advanced model in the field of image animation, it still fails to accurately transfer motion information in videos with significant posture changes of human bodies or faces. For instance, when given a source image with a frontal human body and a driving frame with a side or back human body, FOMM cannot accurately generate frames with the correct posture, as shown in Figure 2a,b. Additionally, FOMM often generates images with ghost effects (false object shadow), as depicted in Figure 2c,d. Furthermore, as illustrated in Figure 3, some keypoints of the target object predicted by FOMM are located on the background, which potentially causes the incorrect parts in the generated frames. In order to solve these problems and improve the performance, we propose an enhanced model based on FOMM. In the proposed novel framework, we focus on two main contributions. 1. We propose an attention module to optimize the generation of optical flow field, which could improve the precision of the generated optical flow field and obtain a more stable and robust motion representation. 2. We propose a multi-occlusion network to repair the details of the picture from multiple scales, and to obtain more accurate results. We take advantage of the occlusion map's ability to correct the pixel values in the generated images and achieve better visual results. Generally, the advantages of the proposed method can be concluded as following. 1.