Human Motion Imitation using Generative Adversarial Networks

Within a unified framework, we handle human image synthesis, including human motion imitation, appearance transfer, and new view synthesis. It indicates that after the model has been trained, it can do all of these jobs. To estimate the human body structure, existing task-specific techniques mostly employ 2D key-points (position). However, they can only represent location data and have no ability to define the person's unique shape or simulate limb rotations. To untangle the position and form, we suggest using a 3D body mesh recovery module in this study. It may define the customized body form as well as model joint placement and rotation. We present a Liquid Warping GAN technique that propagates source information in both image and feature spaces to the synthesized reference in order to retain source information such as texture, style, colour, and face identity. A denoising convolutional auto-encoder extracts the source characteristics in order to accurately characterize the source identity. In addition, our approach allows for more flexible warping from many sources. A one/few-shot adversarial learning is used to increase the generalization capacity of the unseen source pictures. In particular, it begins by putting a model through a rigorous training process. The model is then fine-tuned in a self-supervised manner by using one/few unseen images to create high-resolution (512x512 and 1024x1024) outputs. In addition, we created the imitation dataset to assess human motion imitation and unique view synthesis. Extensive testing has shown that our approaches work better in retaining facial identification, form consistency, and outfit details. Keywords: Human Image Synthesis, Motion Imitation, Novel View Synthesis, Generative Adversarial Network