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Video Generation Models: A Survey of Post-Training and Alignment

This survey explores post-training and alignment techniques in video generation models, highlighting challenges like temporal coherence and physical constraints. It categorizes methods into supervised fine-tuning, self-training, preference-based approaches, and inference-time strategies, while discussing datasets, evaluation practices, and open challenges.

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PublishedSeptember 30, 2026Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni
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WHY IT MAY MATTER

This survey provides a structured overview of techniques to improve video generation models' alignment with human intent and temporal consistency, useful for researchers developing controllable video generation systems.

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