WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911214923153408 |
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| author | Zhu, Ziyue Wu, Zhanqian Zhu, Zhenxin Zhou, Lijun Sun, Haiyang Wan, Bing Ma, Kun Chen, Guang Ye, Hangjun Xie, Jin Yang, jian |
| author_facet | Zhu, Ziyue Wu, Zhanqian Zhu, Zhenxin Zhou, Lijun Sun, Haiyang Wan, Bing Ma, Kun Chen, Guang Ye, Hangjun Xie, Jin Yang, jian |
| contents | Recent advances in driving-scene generation and reconstruction have demonstrated significant potential for enhancing autonomous driving systems by producing scalable and controllable training data. Existing generation methods primarily focus on synthesizing diverse and high-fidelity driving videos; however, due to limited 3D consistency and sparse viewpoint coverage, they struggle to support convenient and high-quality novel-view synthesis (NVS). Conversely, recent 3D/4D reconstruction approaches have significantly improved NVS for real-world driving scenes, yet inherently lack generative capabilities. To overcome this dilemma between scene generation and reconstruction, we propose WorldSplat, a novel feed-forward framework for 4D driving-scene generation. Our approach effectively generates consistent multi-track videos through two key steps: (i) We introduce a 4D-aware latent diffusion model integrating multi-modal information to produce pixel-aligned 4D Gaussians in a feed-forward manner. (ii) Subsequently, we refine the novel view videos rendered from these Gaussians using a enhanced video diffusion model. Extensive experiments conducted on benchmark datasets demonstrate that WorldSplat effectively generates high-fidelity, temporally and spatially consistent multi-track novel view driving videos. Project: https://wm-research.github.io/worldsplat/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving Zhu, Ziyue Wu, Zhanqian Zhu, Zhenxin Zhou, Lijun Sun, Haiyang Wan, Bing Ma, Kun Chen, Guang Ye, Hangjun Xie, Jin Yang, jian Computer Vision and Pattern Recognition Recent advances in driving-scene generation and reconstruction have demonstrated significant potential for enhancing autonomous driving systems by producing scalable and controllable training data. Existing generation methods primarily focus on synthesizing diverse and high-fidelity driving videos; however, due to limited 3D consistency and sparse viewpoint coverage, they struggle to support convenient and high-quality novel-view synthesis (NVS). Conversely, recent 3D/4D reconstruction approaches have significantly improved NVS for real-world driving scenes, yet inherently lack generative capabilities. To overcome this dilemma between scene generation and reconstruction, we propose WorldSplat, a novel feed-forward framework for 4D driving-scene generation. Our approach effectively generates consistent multi-track videos through two key steps: (i) We introduce a 4D-aware latent diffusion model integrating multi-modal information to produce pixel-aligned 4D Gaussians in a feed-forward manner. (ii) Subsequently, we refine the novel view videos rendered from these Gaussians using a enhanced video diffusion model. Extensive experiments conducted on benchmark datasets demonstrate that WorldSplat effectively generates high-fidelity, temporally and spatially consistent multi-track novel view driving videos. Project: https://wm-research.github.io/worldsplat/ |
| title | WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.23402 |