SkyReels-V3 Technique Report
Video generation serves as a cornerstone for building world models, where multimodal contextual inference stands as the defining test of capability. In this end, we present SkyReels-V3, a conditional video generation model, built upon a unified multimodal in-context learning framework with diffusion Transformers. SkyReels-V3 model supports three core generative paradigms within a single architecture: reference images-to-video synthesis, video-to-video extension and audio-guided video generation. (i) reference images-to-video model is designed to produce high-fidelity videos with strong subject identity preservation, temporal coherence, and narrative consistency. To enhance reference adherence and compositional stability, we design a comprehensive data processing pipeline that leverages cross frame pairing, image editing, and semantic rewriting, effectively mitigating copy paste artifacts. During training, an image video hybrid strategy combined with multi-resolution joint optimization is employed to improve generalization and robustness across diverse scenarios. (ii) video extension model integrates spatio-temporal consistency modeling with large-scale video understanding, enabling both seamless single-shot continuation and intelligent multi-shot switching with professional cinematographic patterns. (iii) Talking avatar model supports minute-level audio-conditioned video generation by training first-and-last frame insertion patterns and reconstructing key-frame inference paradigms. On the basis of ensuring visual quality, synchronization of audio and videos has been optimized.
  Extensive evaluations demonstrate that SkyReels-V3 achieves state-of-the-art or near state-of-the-art performance on key metrics including visual quality, instruction following, and specific aspect metrics, approaching leading closed-source systems. Github: https://github.com/SkyworkAI/SkyReels-V3.
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Debang Li
admin_assigned
2026-01-27T14:35:19.214Z
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Li
Debang
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Debang
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Zhengcong Fei
admin_assigned
2026-01-27T14:35:27.008Z
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zhengcong fei
onion
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onion
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Tuanhui Li
admin_assigned
2026-01-27T14:35:34.618Z
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tuanhuili
tuanhui
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tuanhui
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Yikun Dou
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Zheng Chen
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Jiangping Yang
admin_assigned
2026-01-27T14:35:53.130Z
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Jiangping Yang
JumpingXL
user
JumpingXL
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Mingyuan Fan
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Jingtao Xu
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Jiahua Wang
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Baoxuan Gu
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Mingshan Chang
admin_assigned
2026-01-27T14:36:18.951Z
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Mingshan Chang
shesshan
user
shesshan
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Yuqiang Xie
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Binjie Mao
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Youqiang Zhang
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Nuo Pang
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Hao Zhang
69783b4b026bdf0473116f1c
Yuzhe Jin
69783b4b026bdf0473116f1d
Zhiheng Xu
admin_assigned
2026-01-27T14:36:53.072Z
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Zhiheng Xu
Zonnes
user
Zonnes
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Dixuan Lin
69783b4b026bdf0473116f1f
Guibin Chen
admin_assigned
2026-01-27T14:37:01.680Z
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Guibin Chen
jlu-cgb
user
jlu-cgb
69783b4b026bdf0473116f20
Yahui Zhou
SkyReels-V3 Technique Report
Video generation serves as a cornerstone for building world models, where multimodal contextual inference stands as the defining test of capability. In this end, we present SkyReels-V3, a conditional video generation model, built upon a unified multimodal in-context learning framework with diffusion Transformers. SkyReels-V3 model supports three core generative paradigms within a single architecture: reference images-to-video synthesis, video-to-video extension and audio-guided video generation. (i) reference images-to-video model is designed to produce high-fidelity videos with strong subject identity preservation, temporal coherence, and narrative consistency. To enhance reference adherence and compositional stability, we design a comprehensive data processing pipeline that leverages cross frame pairing, image editing, and semantic rewriting, effectively mitigating copy paste artifacts. During training, an image video hybrid strategy combined with multi-resolution joint optimization is employed to improve generalization and robustness across diverse scenarios. (ii) video extension model integrates spatio-temporal consistency modeling with large-scale video understanding, enabling both seamless single-shot continuation and intelligent multi-shot switching with professional cinematographic patterns. (iii) Talking avatar model supports minute-level audio-conditioned video generation by training first-and-last frame insertion patterns and reconstructing key-frame inference paradigms. On the basis of ensuring visual quality, synchronization of audio and videos has been optimized.
  Extensive evaluations demonstrate that SkyReels-V3 achieves state-of-the-art or near state-of-the-art performance on key metrics including visual quality, instruction following, and specific aspect metrics, approaching leading closed-source systems. Github: https://github.com/SkyworkAI/SkyReels-V3.
SkyReels-V3 is a unified multimodal video generation model that supports reference image-to-video, video-to-video extension, and audio-guided video generation through diffusion Transformers and in-context learning frameworks.
diffusion Transformers multimodal in-context learning framework reference images-to-video synthesis video-to-video extension audio-guided video generation spatio-temporal consistency modeling large-scale video understanding talking avatar model key-frame inference paradigms cross frame pairing semantic rewriting multi-resolution joint optimization image video hybrid strategy