LongCat-Video Technical Report
Authors: name: Meituan LongCat Team; name: Xunliang Cai; name: Qilong Huang; name: Zhuoliang Kang; name: Hongyu Li; name: Shijun Liang; name: Liya Ma; name: Siyu Ren; name: Xiaoming Wei; name: Rixu Xie; name: Tong Zhang
Published: 2025-10-25 07:41:02

Abstract: Video generation is a critical pathway toward world models, with efficient
long video inference as a key capability. Toward this end, we introduce
LongCat-Video, a foundational video generation model with 13.6B parameters,
delivering strong performance across multiple video generation tasks. It
particularly excels in efficient and high-quality long video generation,
representing our first step toward world models. Key features include: Unified
architecture for multiple tasks: Built on the Diffusion Transformer (DiT)
framework, LongCat-Video supports Text-to-Video, Image-to-Video, and
Video-Continuation tasks with a single model; Long video generation:
Pretraining on Video-Continuation tasks enables LongCat-Video to maintain high
quality and temporal coherence in the generation of minutes-long videos;
Efficient inference: LongCat-Video generates 720p, 30fps videos within minutes
by employing a coarse-to-fine generation strategy along both the temporal and
spatial axes. Block Sparse Attention further enhances efficiency, particularly
at high resolutions; Strong performance with multi-reward RLHF: Multi-reward
RLHF training enables LongCat-Video to achieve performance on par with the
latest closed-source and leading open-source models. Code and model weights are
publicly available to accelerate progress in the field.