Pose-Based Human Image Generation Using PATN

This work presents a novel framework for personal image generation by transferring poses from a target image to a source image. Using a Pose Attention Transfer (PAT) network, our approach synthesizes realistic images of a person in the target pose while preserving identity and appearance details from the source image. The PAT network leverages attention mechanisms to focus on key regions, ensuring accurate pose transfer and high-quality texture preservation. Experimental results demonstrate that our method generates visually coherent and realistic images, outperforming existing state-of-the-art techniques. This framework has significant potential for virtual try-on, animation, and video synthesis applications.