MLLM 4D

--- 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} } ```

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