| | --- |
| | 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} |
| | } |
| | ``` |