報告人簡介
Dr. Xinyuan Chen is currently a researcher at the Shanghai Artificial Intelligence Lab, collaborating closely with Prof. Yu Qiao. During 2020-2022, she did her post-doc research at East China Normal University, supervised by Prof. Yue Lu. In 2020, she completed her dual PhD from Shanghai Jiao Tong University and the University of Technology Sydney, under the supervision of Prof. Xiaokang Yang and Prof. Dacheng Tao. Her research interests lie in generative models, diffusion models, and generative adversarial networks. Currently, she focuses her work on image and video generation, large-scale video generation models, as well as controllable generation incorporating multi-modality and semantic conditions.
內容簡介
As OpenAI introduces Sora, a generative text-to-video diffusion model, it opens the door to generation of high-definition videos at the minute level from text descriptions. This groundbreaking model not only showcases the vast potential of video generation but also captures the attention of researchers and enthusiasts alike. In this talk, we will delve into the evolutionary path of large-scale video generation models and explore key research milestones. We will then analyze the technical advancements and breakthrough effects achieved by the Sora model. However, while the success of Sora, there still exist limitations and bottlenecks in video generation. Concluding the talk, we will discuss the challenges faced in video generation and explore potential avenues for future breakthroughs.
