Generating Diverse Facial Expressions and Orientations from a Single Image Using Conditional Generative Adversarial Networks

This paper offers a unique deep-learning model for manipulating facial expressions and image orientations, leveraging the power of conditional Generative Adversarial Networks. Building upon the Pix2Pix architecture, renowned for its efficacy in image-to-image translation tasks, our model learns intricate mappings between paired images-an input face and its corre-sponding target image with the desired expression and orientation. We delve into our model's architecture and training regime, emphasizing its ability to disentangle and manipulate facial attributes. Trained and evaluated the IIITM Face Data dataset, our model demonstrates promising results in generating modified facial images exhibiting the target expressions and orientations. We explore the potential applications of this technology, spanning entertainment, human-computer interaction, assistive technology, and psychology. We aim to make impactful advancements in facial image manipulation by addressing ethical considerations.