Introduction:
This is a demonstration of a model for image animation based on a paper to be presented at CVPR 2022. You can generate an animation effect by uploading or shooting a source image and a driving video. You can also choose different micro-motions and data sets to adjust the style of animation. You can click the submit button to run the model, or click the reset button to clear the input. You can also look at some examples or read the paper and Code 2 for more details.
Application scenario:
This model can be used in some scenes that require image animation, such as human face, human body, pixel animation, etc. It can generate an animation effect similar to the source image based on a driving video, without the need to know the structure or pose of the source image in advance. It can be used in some entertainment, creativity, education and other applications. It can also be used for some tasks that require video reconstruction, such as video repair, video enhancement, etc. It can also be used for some tasks that require motion transfer, such as style transfer, expression transfer, etc.
principle:
This model works like this: first, it uses a method called **thin-plate spline** to estimate a more flexible **optical flow**, which can warp the feature map of the source image to drive The feature domain of the video. Then, it utilizes the multi-resolution **occlusion mask** for more efficient feature fusion to recover the missing regions. Finally, it designs some additional auxiliary loss functions to ensure that each module of the network has a clear division of labor, prompting the network to generate high-quality images. In simple terms, this model is to generate an animation effect similar to the source image but with motion in the driving video by learning the motion relationship between the source image and the driving video.
Instructions:
1. Open the URL: yoyo-nb/thin-plate-spline-motion-model – Run with an API on Replicate to upload photos. d:\stablediffusionxl
2. Upload the video. d:\listentothemusic.mp4
3. Select the data model.
4. Submit.
5. Download.
6. Results.
