SkyReels-V3 Technique Report
Authors: name: Debang Li, name: Zhengcong Fei, name: Tuanhui Li, name: Yikun Dou, name: Zheng Chen, name: Jiangping Yang, name: Mingyuan Fan, name: Jingtao Xu, name: Jiahua Wang, name: Baoxuan Gu, name: Mingshan Chang, name: Yuqiang Xie, name: Binjie Mao, name: Youqiang Zhang, name: Nuo Pang, name: Hao Zhang, name: Yuzhe Jin, name: Zhiheng Xu, name: Dixuan Lin, name: Guibin Chen, name: Yahui Zhou
Organization: Skywork

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.