Human-Centered Image Manipulation Using Deep Learning

Abstract: The ability to intuitively manipulate human images with precision is a key requirement in fields like digital art, content creation, and virtual avatars. This project presents a deep learning-based framework for human-centered image manipulation, allowing users to interactively control facial features, expressions, and poses using simple point-based inputs. Built upon a generative adversarial network (GAN) architecture, the system enables users to click and drag specific points on a human image to reposition them in real-time, maintaining visual coherence throughout the transformation. Unlike traditional methods that rely on semantic labels or 3D models, our approach employs feature-guided motion supervision and point tracking within the latent space of StyleGAN2 to achieve fine-grained, photo-realistic editing. A key enhancement in this work is the integration of real image support through GAN inversion, enabling users to upload actual human photos for personalized manipulation. Experimental evaluations demonstrate the system's effectiveness across various human-centered attributes, achieving smooth and realistic results. This work contributes an intuitive, flexible, and efficient solution for interactive human image editing using deep generative models.