mlsanityData to 7 Oct 2026

When and Where do Events Switch in Multi-Event Video Generation?

arXiv 2510.03049 ↗First posted 3 Oct 2025cs.CV cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Text-to-video (T2V) generation has surged in response to challenging questions, especially when a long video must depict multiple sequential events with temporal coherence and controllable content. Existing methods that extend to multi-event generation omit an inspection of the intrinsic factor in event shifting. The paper aims to answer the central question: When and where multi-event prompts control event transition during T2V generation. This work introduces MEve, a self-curated prompt suite for evaluating multi-event text-to-video (T2V) generation, and conducts a systematic study of two representative model families, i.e., OpenSora and CogVideoX. Extensive experiments demonstrate the importance of early intervention in denoising steps and block-wise model layers, revealing the essential factor for multi-event video generation and highlighting the possibilities for multi-event conditioning in future models.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 39 references that are in the map. First posted on arXiv 3 Oct 2025.

Builds on

31 of 39 references.

References · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  5. 2022Scalable Diffusion Models with Transformerscited by 5,931
  6. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  7. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  8. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
  9. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  10. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
  11. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,597
  12. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
19 more
  1. 2025FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Spacecited by 960
  2. 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 958
  3. 2023PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesiscited by 854
  4. 2024Open-Sora: Democratizing Efficient Video Production for Allcited by 637
  5. 2024Latte: Latent Diffusion Transformer for Video Generationcited by 536
  6. 2023InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generationcited by 488
  7. 2024Lumiere: A Space-Time Diffusion Model for Video Generationcited by 450
  8. 2024StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generationcited by 240
  9. 2024Deep Compression Autoencoder for Efficient High-Resolution Diffusion Modelscited by 209
  10. 2025Open-Sora 2.0: Training a Commercial-Level Video Generation Model in 200kcited by 157
  11. 2024MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captionscited by 144
  12. 2024FIFO-Diffusion: Generating Infinite Videos from Text without Trainingcited by 134
  13. 2024Training-Free Consistent Text-to-Image Generationcited by 123
  14. 2021Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Trainingcited by 96
  15. 2024Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Modelscited by 41
  16. 2024Mind the Time: Temporally-Controlled Multi-Event Video Generationcited by 39
  17. 2025ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Modelscited by 37
  18. 2024StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generationcited by 27
  19. 2025Demystifying Flux Architecturecited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026TunerDiT: Training-free Progressive Steering of Diffusion Transformer for Multi-Event Video Generationcited by 1