Training a video generation AI from scratch is a daunting task, often overshadowed by the impressive results of cutting-edge models like Sora or Veo. However, delving into the underlying process of building a basic experimental video model reveals a intricate workflow. Compared to image generation, video production introduces a multitude of additional challenges, including understanding motion, consistency, timing, frame-by-frame object changes, camera movement, physics, and temporal coherence.

These complexities raise questions about the primary bottlenecks in video generation AI development. Is it the computational power required, the availability of video data, the architecture of the model, the evaluation metrics, or simply the fact that video has more dynamic components than images? Understanding these challenges is crucial to advancing the field of video generation AI.

Photo by Sparki Arun on Pexels
Photos provided by Pexels