This is the official PyTorch implementation of the ICLR 2022 paper “Latent Image Animator: Learning to Animate Images via Latent Space Navigation”
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DISCLAIMER: This is only for testing purposes and can only be used where strict consent has been given. Do not use this for illegal purposes
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Posted by: @ESPYER
Latent picture Animator (LIA) is utilised by deep fake creation in order to permit picture animation. This is accomplished with the assistance of a tutorial video. It is necessary to acquire knowledge of a set of orthogonal motion directions in latent space in order to accomplish this. By utilising this strategy, the need for explicit structural representation, which is both costly and computationally difficult, is eliminated. LIA is able to generate animation that is lifelike by exploring the latent space, which enables it to transfer motion from one video to another in an effective manner.
Through the utilisation of the latent space, which is a compressed representation of images, LIA has the potential to deliver realistic motion renderings. It does not rely on the structural information that is recovered from driving videos; rather, it directly manipulates latent codes in order to simulate movement.
By encoding latent routes as a linear combination of learnt motion directions, ESPY LIA is able to facilitate the deep fake creation description of a wide range of motion types while reducing the number of computations required. In comparison to other methods, LIA produces outcomes that are superior in terms of maintaining the face structure and the quality of the image.
