Stylized Video Generation via Decoupled Data Synthesis and Gated Style Token Injection

Stylized video generation requires synthesizing dynamic con-tent that reflects the artistic characteristics of a reference image whilemaintaining semantic control and temporal coherence. Existing fine-tuning-based approaches suffer from two recurring limitations: motion degrada-tion caused by training on static image stylization datasets, which lacktemporal supervision and erode the temporal priors of pre-trained back-bones; and a representation bottleneck from encoder-based style injec-tion, which discards fine-grained stylistic details and bounds stylizationquality by the encoder’s representational capacity. We address both limi-tations through a unified pipeline that covers dataset construction, modelarchitecture, and training strategy. Specifically, we construct MoStyle-5K, a stylized video dataset of 5,000 videos spanning 27 artistic stylesand 4,000 preference pairs, built via a decoupled content-motion syn-thesis pipeline that explicitly preserves temporal priors in the supervi-sion signal. Building on this, we propose Implicit Gated Style To-ken Injection (IGST), which directly concatenates style patches as to-kens to bypass the encoder bottleneck, disables RoPE for style tokensto remove spatial alignment bias, and applies head-wise gated atten-tion to suppress residual structural correlations. The framework is opti-mized with a hybrid image-video objective and refined via Diffusion-DPO post-training using the structured preference pairs in MoStyle-5K. Experiments show that our method achieves superior style fidelityand motion dynamics compared with existing baselines. Project page:https://sixi111.github.io/MoStyle/.