FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation
Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retaining all previous content becomes increasingly expensive and redundant, making effective historical selection essential. Existing approaches often determine historical relevance based on the current content. However, information rel
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Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retaining all previous content becomes increasingly expensive and redundant, making effective historical selection essential. Existing approaches often determine historical relevance based on the current content. However, information rel
This research addresses a critical challenge in long-horizon video generation by proposing a novel method for efficient historical frame selection. For ML engineers, understanding such techniques is crucial for developing scalable and performant video generation models, especially as video content becomes more prevalent and complex.