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

arXiv:2610.00812 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/people-robots/Awesome-Video-Generation-Post-Training

Terminology

• Alignment — 330× in this paper · explained in 1 episode
• Consistency — 94× in this paper · explained in 3 episodes
• Distillation — 82× in this paper · explained in 2 episodes
• Fine-Tuning — 77× in this paper · explained in 5 episodes
• Benchmark — 72× in this paper · explained in 2 episodes
• Diffusion Model — 72× in this paper · explained in 8 episodes
• Adaptation — 62× in this paper · explained in 1 episode
• Diffusion Models — 55× in this paper · explained in 6 episodes
• Reinforcement Learning — 46× in this paper · explained in 4 episodes
• Transformer — 41× in this paper · explained in 2 episodes
• Fidelity — 35× in this paper · explained in 2 episodes
• Personalization — 28× in this paper · explained in 2 episodes
• GRPO — 27× in this paper · explained in 6 episodes
• International Conference on Computer Vision — 23× in this paper
• Knowledge Distillation — 23× in this paper · explained in 11 episodes
• Temporal consistency — 23× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 18× in this paper
• Domain Adaptation — 18× in this paper · explained in 5 episodes
• Robustness — 17× in this paper · explained in 5 episodes
• Alignment Methods — 15× in this paper · explained in 1 episode
• Iterative Refinement — 15× in this paper · explained in 3 episodes
• Benchmarking — 13× in this paper · explained in 3 episodes
• Steering — 12× in this paper · explained in 2 episodes
• Generative Model — 11× in this paper · explained in 2 episodes
• Generative Models — 11× in this paper · explained in 2 episodes
• Latent Space — 10× in this paper · explained in 7 episodes
• Physical Constraints — 10× in this paper · explained in 7 episodes
• Alignment Dimensions — 8× in this paper · explained in 1 episode
• Core — 8× in this paper · explained in 2 episodes
• World Model — 8× in this paper · explained in 7 episodes
• Flow Matching — 7× in this paper · explained in 7 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Direct Preference Optimization — 6× in this paper
• Distribution Matching — 6× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Semantic Alignment — 6× in this paper · explained in 3 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Domain Shift — 5× in this paper · explained in 4 episodes
• Evaluation Dimensions — 5× in this paper · explained in 3 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Instruction Tuning — 5× in this paper · explained in 2 episodes
• Credit assignment — 4× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 4× in this paper
• European Conference on Computer Vision — 4× in this paper
• Grounding — 4× in this paper · explained in 3 episodes
• Optical Flow — 4× in this paper · explained in 2 episodes
• Reward Hacking — 4× in this paper · explained in 8 episodes
• Winter Conference on Applications of Computer Vision — 4× in this paper
• Alignment Techniques — 3× in this paper · explained in 1 episode
• Computational overhead — 3× in this paper · explained in 2 episodes
• Decoupling — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Group Relative Policy Optimization — 3× in this paper
• Group Relative Policy Optimization (GRPO) — 3× in this paper · explained in 5 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Reward Function — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Affordance — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Association for Computational Linguistics — 2× in this paper
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Consistency Distillation — 2× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 2× in this paper · explained in 9 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Medical Image Computing and Computer-Assisted Intervention — 2× in this paper
• Prompt Adherence — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Verifiable Rewards — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Alignment Problem — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Distribution Matching Distillation — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Formal Verification — 1× in this paper · explained in 2 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes

Sources

• MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Open-Sora Plan: Open-Source Large Video Generation Model
• VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
• RDPO: Real Data Preference Optimization for Physics Consistency Video Generation
• Beyond Reward Margin: Rethinking and Resolving Likelihood Displacement in Diffusion Models via Video Generation
• Zo3T: Zero-Shot 3D-Aware Trajectory-Guided Image-to-Video Generation via Test-Time Training
• InstanceV: Instance-Level Video Generation
• Controllable Video Generation: A Survey
• A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights
• Show Me: Unifying Instructional Image and Video Generation with Diffusion Models
• GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning
• ATI: Any Trajectory Instruction for Controllable Video Generation
• Populate-A-Scene: Affordance-Aware Human Video Generation
• OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation
• Consistent Video Editing as Flow-Driven Image-to-Video Generation
• LoRA-Edit: Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning
• PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation
• Radial Attention: $O(n\log n)$ Sparse Attention with Energy Decay for Long Video Generation

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