AI RESEARCH
Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute
arXiv CS.CV
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ArXi:2504.17816v3 Announce Type: replace Subject-driven video generation (SDV-Gen) aims to produce videos of a specific subject by adapting a pretrained video model, enabling personalized and application-driven content creation. To achieve this goal, per-subject tuning methods require approximately 200 A100 GPU hours to generate a customized video, whereas zero-shot methods avoid per-subject tuning but typically rely on millions of subject-video pairs for the supervision, incurring massive network fine-tuning costs (10K-200K A100 GPU hours.