Style4D-Bench: A Benchmark Suite for 4D Stylization

Beiqi Chen^*1,2  Shuai Shao^*2  Haitang Feng^3,2  Jianhuang Lai⁴  Jianlou Si^5†  Guangcong Wang^2†

¹Harbin Institute of Technology  ²Vision, Graphics, and X Group, Great Bay University
³Nanjing University  ⁴Sun Yat-Sen University  ⁵Alibaba Group

^*Equal Contribution     ^†Corresponding Authors

🐤 Project | YouTube | arXiv

🐤 Abstract: We introduce Style4D-Bench, the first benchmark suite specifically designed for 4D stylization, with the goal of standardizing evaluation and facilitating progress in this emerging area. Style4D-Bench comprises: 1) a strong baseline that make an initial attempt for 4D stylization, 2) a comprehensive evaluation protocol measuring spatial fidelity, temporal coherence, and multi-view consistency through both perceptual and quantitative metrics, and 3) a curated collection of high-resolution dynamic 4D scenes with diverse motions and complex backgrounds. To establish a strong baseline, we present Style4D, a novel framework built upon 4D Gaussian Splatting. It consists of three key components: a basic 4DGS scene representation to capture reliable geometry, a Style Gaussian Representation that leverages lightweight per-Gaussian MLPs for temporally and spatially aware appearance control, and a Holistic Geometry-Preserved Style Transfer module designed to enhance spatio-temporal consistency via contrastive coherence learning and structural content preservation. Extensive experiments on Style4D-Bench demonstrate that Style4D achieves state-of-the-art performance in 4D stylization, producing fine-grained stylistic details with stable temporal dynamics and consistent multi-view rendering. We expect Style4D-Bench to become a valuable resource for benchmarking and advancing research in stylized rendering of dynamic 3D scenes.

🐤 Framework Overview: Style4D consists of three key components, a basic 4DGS representation, a Style Gaussian Representation, and a Holistic Geometry-preserved Style Transfer. We first train a basic 4DGS representation with the content image to obtain 4D scene geometry. Then we propose a new Style Gaussian Representation for 4D stylization. We also introduce a Holistic Geometry-preserved Style Transfer module to improve consistency and quality of stylization.

🐤 Evaluation Metrics: Quantitative comparisons of our proposed Style4D against state-of-the-art methods on Style4D-Bench.

Citation

If you find this useful for your research, please cite the our paper.

@misc{chen2025style4dbenchbenchmarksuite4d,
      title={Style4D-Bench: A Benchmark Suite for 4D Stylization}, 
      author={Beiqi Chen and Shuai Shao and Haitang Feng and Jianhuang Lai and Jianlou Si and Guangcong Wang},
      year={2025},
      eprint={2508.19243},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.19243}, 
}

Acknowledgement

The computational resources are supported by SongShan Lake HPC Center (SSL-HPC) in Great Bay University. This work was also supported by Guangdong Research Team for Communication and Sensing Integrated with Intelligent Computing (Project No. 2024KCXTD047).