mlsanityData to 7 Oct 2026

SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model

arXiv 2602.21818 ↗First posted 25 Feb 2026cs.CVcited by 11

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Abstract (arXiv)

SkyReels V4 is a unified multi modal video foundation model for joint video audio generation, inpainting, and editing. The model adopts a dual stream Multimodal Diffusion Transformer (MMDiT) architecture, where one branch synthesizes video and the other generates temporally aligned audio, while sharing a powerful text encoder based on the Multimodal Large Language Models (MLLM). SkyReels V4 accepts rich multi modal instructions, including text, images, video clips, masks, and audio references. By combining the MLLMs multi modal instruction following capability with in context learning in the video branch MMDiT, the model can inject fine grained visual guidance under complex conditioning, while the audio branch MMDiT simultaneously leverages audio references to guide sound generation. On the video side, we adopt a channel concatenation formulation that unifies a wide range of inpainting style tasks, such as image to video, video extension, and video editing under a single interface, and naturally extends to vision referenced inpainting and editing via multi modal prompts. SkyReels V4 supports up to 1080p resolution, 32 FPS, and 15 second duration, enabling high fidelity, multi shot, cinema level video generation with synchronized audio. To make such high resolution, long-duration generation computationally feasible, we introduce an efficiency strategy: Joint generation of low resolution full sequences and high-resolution keyframes, followed by dedicated super-resolution and frame interpolation models. To our knowledge, SkyReels V4 is the first video foundation model that simultaneously supports multi-modal input, joint video audio generation, and a unified treatment of generation, inpainting, and editing, while maintaining strong efficiency and quality at cinematic resolutions and durations.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 37 of its 46 references that are in the map. First posted on arXiv 25 Feb 2026.

Builds on

37 of 46 references.

References · 37

  1. 2022Scalable Diffusion Models with Transformerscited by 5,931
  2. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,536
  3. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
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  6. 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
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  11. 2025MAGI-1: Autoregressive Video Generation at Scalecited by 262
  12. 2016SoundNet: Learning Sound Representations from Unlabeled Videocited by 244
25 more
  1. 2022MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generationcited by 225
  2. 2025SkyReels-V2: Infinite-length Film Generative Modelcited by 218
  3. 2026LTX-2: Efficient Joint Audio-Visual Foundation Modelcited by 213
  4. 2024Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generationcited by 200
  5. 2020TransNet V2: An effective deep network architecture for fast shot transition detectioncited by 166
  6. 2024Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Contentcited by 132
  7. 2025OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Modelscited by 127
  8. 2025VSA: Faster Video Diffusion with Trainable Sparse Attentioncited by 100
  9. 2025JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronizationcited by 84
  10. 2024OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generationcited by 77
  11. 2025Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generationcited by 76
  12. 2025Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Modelcited by 73
  13. 2025SkyReels-A2: Compose Anything in Video Diffusion Transformerscited by 72
  14. 2025Let Them Talk: Audio-Driven Multi-Person Conversational Video Generationcited by 64
  15. 2025UniVerse-1: Unified Audio-Video Generation via Stitching of Expertscited by 58
  16. 2024VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Modelscited by 40
  17. 2024AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generationcited by 39
  18. 2025SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformerscited by 33
  19. 2025OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulationcited by 30
  20. 2024MMDisCo: Multi-Modal Discriminator-Guided Cooperative Diffusion for Joint Audio and Video Generationcited by 23
  21. 2025Kling-Avatar: Grounding Multimodal Instructions for Cascaded Long-Duration Avatar Animation Synthesiscited by 20
  22. 2025SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformerscited by 13
  23. 2022SyncNet: correlating objective for time delay estimation in audio signalscited by 9
  24. 2025Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interactioncited by 9
  25. 2026Apollo: Unified Multi-Task Audio-Video Joint Generationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 11.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026SkyReels-V3 Technique Report
  2. 2026MoRoute: Dynamic Routing for In-Context Multimodal Video Generation
  3. 2026MMControl: Unified Multi-Modal Control for Joint Audio-Video Generation
  4. 2025Audio-Guided Visual Editing with Complex Multi-Modal Prompts
  5. 2025UniVideo: Unified Understanding, Generation, and Editing for Videos
  6. 2025AV-Edit: Multimodal Generative Sound Effect Editing via Audio-Visual Semantic Joint Control
  7. 2023CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling
  8. 2026VINO: A Unified Visual Generator with Interleaved OmniModal Context