Abstract:
"This project presents a novel approach to synthesizing human animations from single images using Generative Adversarial Networks (GANs). The study addresses challenges in accurately animating clothing and poses, vital for realistic 2D human animations. By leveraging GAN architectures, which include a generator and discriminator, the system synthesizes animations that capture fine-grained details of apparel and human motion. Advanced deep learning techniques, such as convolutional neural networks and adversarial training, are employed to optimize the model's performance.
The methodology emphasizes data preprocessing, including pose extraction and cloth detail mapping, to ensure high fidelity and temporal consistency in the animations. Testing relies on qualitative assessments, as conventional quantitative metrics for animation realism are currently unavailable. The generated animations have potential applications in entertainment, fashion, and virtual reality, providing an efficient and cost-effective alternative to traditional methods like 3D modeling and motion capture.
This research contributes to the domains of AI and animation by enhancing GAN model architectures to improve the realism of cloth and motion synthesis. It highlights the ability to integrate dynamic apparel and pose interactions seamlessly, setting a foundation for future advancements in human animation synthesis."
