Improve model card with metadata and paper links

README.md

@@ -1,7 +1,38 @@
----
-license: apache-2.0
-
----
-
-UniFuture
-
+---
+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}
+}
+```