First Comprehensive Survey of Post-Training and Alignment for Video Generation Models
Arizona-State-University · hf · 2026-10-03
Arizona State University releases the first comprehensive survey on post-training and alignment in video generation. It frames post-training as a unifying paradigm, splits methods into four categories (supervised fine-tuning, self-training/distillation, preference/reward-based, and inference-time), and covers unique video challenges like temporal error accumulation and motion-appearance coupling, plus datasets, benchmarks, and open problems such as scalable reward design and long-horizon consistency.
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