| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-video |
| tags: |
| - autonomous-driving |
| - world-model |
| - computer-vision |
| - 4D |
| --- |
| |
| # UniFuture: A 4D Driving World Model for Future Generation and Perception |
|
|
| UniFuture is a unified 4D Driving World Model designed to simulate the dynamic evolution of the 3D physical world. Unlike existing driving world models that focus solely on 2D pixel-level video generation or static perception, UniFuture bridges appearance and geometry to construct a holistic 4D representation. |
|
|
| - **Paper:** [UniFuture: A 4D Driving World Model for Future Generation and Perception](https://arxiv.org/abs/2503.13587) |
| - **Project Page:** [https://dk-liang.github.io/UniFuture/](https://dk-liang.github.io/UniFuture/) |
| - **Repository:** [https://github.com/dk-liang/unifuture](https://github.com/dk-liang/unifuture) |
|
|
| ## Introduction |
|
|
| UniFuture treats future RGB images and depth maps as coupled projections of the same 4D reality and models them jointly within a single framework. To achieve this, it introduces two key components: |
| - **Dual-Latent Sharing (DLS):** A scheme that maps visual and geometric modalities into a shared spatio-temporal latent space, implicitly entangling texture with structure. |
| - **Multi-scale Latent Interaction (MLI):** A mechanism that enforces bidirectional consistency: geometry constrains visual synthesis to prevent structural hallucinations, while visual semantics refine geometric estimation. |
|
|
| During inference, UniFuture can forecast high-fidelity, geometrically consistent 4D scene sequences (image-depth pairs) from a single current frame. |
|
|
| ## Citation |
|
|
| If you find this work useful in your research, please consider citing: |
|
|
| ```bibtex |
| @inproceedings{liang2026UniFuture, |
| title={UniFuture: A 4D Driving World Model for Future Generation and Perception}, |
| author={Liang, Dingkang and Zhang, Dingyuan and Zhou, Xin and Tu, Sifan and Feng, Tianrui and Li, Xiaofan and Zhang, Yumeng and Du, Mingyang and Tan, Xiao and Bai, Xiang}, |
| booktitle={IEEE International Conference on Robotics and Automation}, |
| year={2026} |
| } |
| ``` |
