Generative AI Challenge

Generative AI research currently grapples with improving the quality, diversity, and efficiency of generated outputs across various tasks, such as image matting and story visualization. This involves exploring novel architectures like diffusion models and GANs, often incorporating techniques such as character guidance and caption augmentation to enhance control and consistency. Addressing challenges like bias, misinformation, and computational cost is central to making generative AI more reliable and applicable in real-world scenarios, fostering a symbiotic relationship between human values and AI development.