Long Context Tuning for Video Generation
On the map: Diffusion models → Video generation
Abstract (arXiv)
Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-shot scenes with visual and dynamic consistency across shots. In this work, we introduce Long Context Tuning (LCT), a training paradigm that expands the context window of pre-trained single-shot video diffusion models to learn scene-level consistency directly from data. Our method expands full attention mechanisms from individual shots to encompass all shots within a scene, incorporating interleaved 3D position embedding and an asynchronous noise strategy, enabling both joint and auto-regressive shot generation without additional parameters. Models with bidirectional attention after LCT can further be fine-tuned with context-causal attention, facilitating auto-regressive generation with efficient KV-cache. Experiments demonstrate single-shot models after LCT can produce coherent multi-shot scenes and exhibit emerging capabilities, including compositional generation and interactive shot extension, paving the way for more practical visual content creation. See https://guoyww.github.io/projects/long-context-video/ for more details.
Key facts. Cited by 113 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 58 of its 80 references that are in the map. First posted on arXiv 13 Mar 2025.
Builds on
58 of 80 references.
Closest ancestors · 30
Same direction, newest first
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Other references · 28
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- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
- 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
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- 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 484
- 2022MAGVIT: Masked Generative Video Transformercited by 346
- 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 301
- 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 255
- 2023Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolutioncited by 229
- 2025History-Guided Video Diffusioncited by 171
- 2024In-Context LoRA for Diffusion Transformerscited by 158
- 2023Make Pixels Dance: High-Dynamic Video Generationcited by 140
- 2023Animate-A-Story: Storytelling with Retrieval-Augmented Video Generationcited by 90
- 2020A Local-to-Global Approach to Multi-modal Movie Scene Segmentationcited by 60
- 2024Mind the Time: Temporally-Controlled Multi-Event Video Generationcited by 40
- 2024TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generationcited by 25
- 2024Synchronized Video Storytelling: Generating Video Narrations with Structured Storylinecited by 19
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Citing papers per year: 2025: 54; 2026: 59.
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