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<sup>1</sup>Department of Automation, Tsinghua University <sup>2</sup>GigaAI
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<i><sup>*</sup>Equal Contribution <sup>†</sup>
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<p align="center">
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<a href="https://github.com/Zhangyr2022/MoGe4D"><img src="https://img.shields.io/badge/GitHub-Code-black?logo=github" alt="GitHub"></a>
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<a href="https://huggingface.co/Yanran21/MoGe4D"><img src="https://img.shields.io/badge/🤗%20HuggingFace-Model-ffd21e" alt="HuggingFace Model"></a>
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<a href="https://www.modelscope.cn/datasets/YanranZhang/TrajScene-60K"><img src="https://img.shields.io/badge/🤖%20ModelScope-Dataset-4e29ff" alt="ModelScope Dataset"></a>
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</p>
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## 📄 Paper Summary
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Generating interactive and dynamic 4D scenes from a single static image is a core challenge. Existing methods decouple geometry from motion — either *generate-then-reconstruct* (geometric inconsistency) or *reconstruct-then-generate* (limited, externally-constrained motion) — causing spatiotemporal inconsistency and poor generalization.
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<sup>1</sup>Department of Automation, Tsinghua University <sup>2</sup>GigaAI
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<i><sup>*</sup>Equal Contribution <sup>†</sup>Corresponding Author</i>
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<p align="center">
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<a href="https://github.com/Zhangyr2022/MoGe4D"><img src="https://img.shields.io/badge/GitHub-Code-black?logo=github" alt="GitHub"></a>
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<a href="https://huggingface.co/Yanran21/MoGe4D"><img src="https://img.shields.io/badge/🤗%20HuggingFace-Model-ffd21e" alt="HuggingFace Model"></a>
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<a href="https://www.modelscope.cn/datasets/YanranZhang/TrajScene-60K"><img src="https://img.shields.io/badge/🤖%20ModelScope-Dataset-4e29ff" alt="ModelScope Dataset"></a>
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</p>
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## 📄 Paper Summary
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Generating interactive and dynamic 4D scenes from a single static image is a core challenge. Existing methods decouple geometry from motion — either *generate-then-reconstruct* (geometric inconsistency) or *reconstruct-then-generate* (limited, externally-constrained motion) — causing spatiotemporal inconsistency and poor generalization.
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