TasteRoute: Personalized Routing for Video Generation

Rapid progress in video generation has produced a growing ecosystem of models that differ substantially in capability and generation cost. This raises a natural question: can each request be efficiently routed to an appropriate model? We first investigate whether video router can be learned from the generation input alone. We find that once outputs clear a basic quality bar, annotators' own favorites agree with the consensus pick only 34-55% of the time. Motivated by this observation, we introduce TasteRoute, a personalized video-generation router that selects a generator jointly based on input request, user preferences, and available generation budget. Across text-to-video and image-to-video settings, TasteRoute achieves competitive preference-routing performance with strong simple baselines while reducing average generation cost, with larger savings under higher budget caps. Finally, we release TasteRoute-3k, a human-annotated dataset containing multi-model video comparisons, quality judgments, preference rankings, and user-profile signals to facilitate future research on personalized and cost-aware video routing.