• New State-of-the-art Method Produces Realistic Image Animation

    A group of researchers from the University of Trento has proposed a new state-of-the-art method for highly-realistic image animation.

    The problem of image animation has gained more and more attention during the past years, and along with that a large number of applications and use-cases for image animation models have appeared. Researchers led by Aliaksandr Siarohin have designed a new self-supervised learning framework for learning an image motion model from data.


    Results of the method on the VoxCeleb dataset.

    Within this framework, the motion and appearance information is decoupled and learned in a completely self-supervised manner. The proposed method works by detecting keypoints together with local affine transformations which are used to define a motion representation altogether. This representation is learned in a fully unsupervised manner from a source frame using a “keypoint detector” network. Besides the source frame, the method takes another, so-called “driving frame” as input. A dense motion network consumes the source image together with the motion representation to generate a dense optical flow from the “driving” frame to the source frame. In the last stage of the proposed method, a generator module takes the source image and the dense optical flow to render a target image.

    The proposed method was trained and evaluated using 4 different datasets: VoxCeleb, UvA Nemo, BAIR robot pushing, and a custom dataset collected from tai-chi videos from YouTube. Results showed that the method outperforms state-of-the-art methods on all benchmarks.

    Researchers open-sourced the implementation of the new method and it can be found here. The paper was published in the proceedings of NIPS.

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