Beyond Uncanny Valley: Breaking Down Sora
a16z Podcast
Overcoming Challenges in Video Generation
Generating videos involves significant challenges. Firstly, processing multiple images simultaneously requires considerably more compute resources and memory which makes training large-scale models for video data more expensive. Secondly, the lack of publicly available high-quality video datasets poses a data challenge. Unlike image datasets, video data lacks curated, widely-used datasets and obtaining accurate labels and descriptions for videos is problematic. Moreover, videos are complex with intricate relationships between frames, incorporating factors like physics and object permanence. Therefore, training high-capacity models with sufficient compute power and quality data becomes vital, despite the empirical uncertainty around the amount of data and compute needed to discover high-level concepts within the data. The success of video generation models like the SORA model hinges on overcoming these challenges.
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