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MLLM 4D

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)...

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Modellquelle

Art
Other
Dateigröße
7,10–9,23 GB
Version
vModel 00001 of 00002
Aktualisiert
24. Okt. 2026
Quelle
Hugging Face

Quellenbeschreibung

MLLM-4D: Towards Visual-based Spatial-Temporal Intelligence

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.

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 and use the following commands:

# 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:

@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}
}

Quellen

1 Quelle
Hugging FacePrimärVersion 16bb0c579bc480fc02413c05987ae992b38f306b
huggingface.co/flow666/MLLM-4D/tree/16bb0c579bc480fc02413c05987ae992b38f306b

Verifiziert 24. Okt.

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Modellartefakte

4 Artefakte

MLLM-4D-RFT-1.0/model-00001-of-00002.safetensors

safetensors · 9,23 GB · SHA-256 7725db330a10…7c04 · Hugging Face

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MLLM-4D-RFT-1.0/model-00002-of-00002.safetensors

safetensors · 7,10 GB · SHA-256 11683a54c84a…469b · Hugging Face

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MLLM-4D-SFT-1.0/model-00001-of-00002.safetensors

safetensors · 9,23 GB · SHA-256 85ec9e58dc14…1210 · Hugging Face

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MLLM-4D-SFT-1.0/model-00002-of-00002.safetensors

safetensors · 7,10 GB · SHA-256 32bf14500d94…9726 · Hugging Face

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Quellenauszüge

2 Auszüge

--- 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} } ```

Source context: 0 downloads · 1 likes · Pipeline video-text-to-text · Library transformers · Repo flow666/MLLM-4D