Deep learning models have shown that it is possible to train neural networks to dispense, to a lesser or greater extent, with the need for human intervention for the task of image animation, which helps to reduce not only the production time of these audiovisual pieces, but also presents benefits with respect to the economic investment they require to be made. However, these models suffer from two common problems: the animations they generate are of very low resolution and they require large amounts of training data to generate good results. To deal with these issues, this article introduces the architectural modification of a state-of-the-art image animation model integrated with a video super-resolution model to make the generated videos more visually pleasing to viewers. Although it is possible to train the animation models with higher resolution images, the time it would take to train them would be much longer, which does not necessarily benefit the quality of the animation, so it is more efficient to complement it with another model focused on improving the animation resolution of the generated video as we demonstrate in our results. We present the design and implementation of a convolutional neural network based on an state-of-art model focused on the image animation task, which is trained with a set of facial data from videos extracted from the YouTube platform. To determine which of all the modifications to the selected state-of-the-art model architecture is better, the results are compared with different metrics that evaluate the performance in image animation and video quality enhancement tasks. The results show that modifying the architecture of the model focused on the detection of characteristic points significantly helps to generate more anatomically and visually attractive videos. In addition, perceptual testing with users shows that using a super-resolution video model as a plugin helps generate more visually appealing videos.