# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
###### Abstract  
Image animation aims to transfer the posture change of a driving video to the static object of the source image, and has potential applications in various domains, such as film and game industries. The essential part in this task is to generate a video by learning the motion from the driving video while preserving the appearance from the source image. As a result, a new object with the same motion will be generated in the animated video. However, it is a significant challenge if the object pose shows large-scale change. Even the most recent method failed to achieve this correctly with good visual effects. In order to solve the problem of poor visual effects in the videos with the large-scale pose change, a novel method based on an improved first-order motion model (FOMM) with enhanced dense motion and repair ability was proposed in this paper. Firstly, when generating optical flow, we propose an attention mechanism that optimizes the feature representation of the image in both channel and spatial domains through maximum pooling. This enables better distortion of the source image into the feature domain of the driving image. Secondly, we further propose a multi-scale occlusion restoration module that generates a multi-resolution occlusion map by upsampling the low-resolution occlusion map. Following this, the generator redraws the occluded part of the reconstruction result across multiple scales through the multi-resolution occlusion map to achieve more accurate and vivid visual effects. In addition, the proposed model can be trained effectively in an unsupervised manner. We evaluated the proposed model on three benchmark datasets. The experimental results showed that multiple evaluation indicators were improved by our proposed method, and the visual effect of the animated videos obviously outperformed the FOMM. On the Voxceleb1 dataset, the pixel error, average keypoints distance and average Euclidean distance by our proposed method were reduced by 6.5%, 5.1% and 0.7%, respectively. On the TaiChiHD dataset, the pixel error, average keypoints distance and missing keypoints rate measured by our proposed method were reduced by 4.9%, 13.5% and 25.8%, respectively. image animation; convolutional block attention module; first order motion model; generative adversarial networks +
Footnote †: journal: Medical Image and Image Analysis  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Y., [PERSON], [PERSON], Y., [PERSON], Y., [PERSON], Y., [PERSON], Y., [PERSON], Y., [PERSON], Y., Y., Y., Y., Y., Y., Y., Y., YTraditional approaches for image animation typically involve using data fusion from different data sources to acquire prior knowledge of the object [1], (such as a 3D model), and using computer graphics technology to solve the problem [2; 3; 4]. Face2 Face [4] uses a 3D parametric model with 269 parameters to fit facial posture, expression, illumination and shape information. By detecting and tracking the face image and adjusting the relevant parameters, the expression of the face in the source domain is converted into the expression of the face in the target domain. However, this approach is not applicable when the task is not limited to the face. With the development of machine learning and deep learning, many industries have achieved substantial development [5; 6; 7]. Recently, with the development of generative adversarial networks [8] (GANs) and variational auto-encoders [9] (VAEs), many methods have been proposed and have expedited the research and application of image animation. These methods have been used as substitutes for 3D parametric models in generating real images. These methods are mainly divided into two categories.