Add model card for MLLM-4D

Hi! I'm Niels from the Hugging Face community team. This PR adds a model card for MLLM-4D, which includes:
- Metadata for the `video-text-to-text` pipeline and `transformers` library.
- Links to the paper and official GitHub repository.
- A brief description of the model's capabilities in 4D spatiotemporal reasoning.
- Sample usage instructions for running inference as found in the repository.
- Citation information for the paper.

README.md

@@ -0,0 +1,37 @@
+---
+library_name: transformers
+pipeline_tag: video-text-to-text
+---
+
+# MLLM-4D: Towards Visual-based Spatial-Temporal Intelligence
+
+[**MLLM-4D**](https://github.com/GVCLab/MLLM-4D) is a comprehensive framework designed to bridge the gaps in training data curation and model post-training for spatiotemporal understanding and reasoning. It enables multimodal large language models (MLLMs) to perceive and reason about the evolution of 3D space over time from purely visual inputs.
+
+- **Paper:** [MLLM-4D: Towards Visual-based Spatial-Temporal Intelligence](https://huggingface.co/papers/2603.00515)
+- **Repository:** [https://github.com/GVCLab/MLLM-4D](https://github.com/GVCLab/MLLM-4D)
+- **Project Page:** [https://github.com/GVCLab/MLLM-4D](https://github.com/GVCLab/MLLM-4D)
+
+## Model Description
+MLLM-4D achieves state-of-the-art spatiotemporal intelligence by focusing on the relationships between objects and the camera within 3D space. The model establishes foundational 4D understanding via Supervised Fine-Tuning (SFT) and further catalyzes 4D reasoning capabilities by employing Group Relative Policy Optimization (GRPO) with specialized Spatiotemporal Chain of Thought (ST-CoT) prompting. It achieves these capabilities using purely 2D RGB inputs without architectural modifications.
+
+## Usage
+To run the inference demo for MLLM-4D, please refer to the setup instructions in the [official repository](https://github.com/GVCLab/MLLM-4D) and use the following commands:
+
+```bash
+# for MLLM-4D-SFT
+python scripts/inference.py --model_type "MLLM-4D-SFT" --model_path PATH-to-MLLM-4D-SFT
+
+# for MLLM-4D-RFT
+python scripts/inference.py --model_type "MLLM-4D-RFT" --model_path PATH-to-MLLM-4D-RFT
+```
+
+## Citation
+If you find the work useful, please consider citing:
+```bibtex
+@article{yin2026mllm4d,
+    title={MLLM-4D: Towards Visual-based Spatial-Temporal Intelligence},
+    author={Yin, Xingyilang and Li, Chengzhengxu and Chang, Jiahao and Pun, Chi-Man and Cun, Xiaodong},
+    journal={arXiv preprint arXiv:2603.00515},
+    year={2026}
+}
+```