ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866914402812297216 |
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| author | Wang, Kaixuan Chen, Tianxing Liu, Jiawei Su, Honghao Zhu, Shaolong Wang, Minxuan Li, Zixuan Chen, Yue Gao, Huan-ang Qin, Yusen Wang, Jiawei Zhang, Qixuan Xu, Lan Yu, Jingyi Mu, Yao Luo, Ping |
| author_facet | Wang, Kaixuan Chen, Tianxing Liu, Jiawei Su, Honghao Zhu, Shaolong Wang, Minxuan Li, Zixuan Chen, Yue Gao, Huan-ang Qin, Yusen Wang, Jiawei Zhang, Qixuan Xu, Lan Yu, Jingyi Mu, Yao Luo, Ping |
| contents | Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital assets, in both scale and diversity. In this work, we present ManiTwin, an automated and efficient pipeline for generating data-generation-ready digital object twins. Our pipeline transforms a single image into simulation-ready and semantically annotated 3D asset, enabling large-scale robotic manipulation data generation. Using this pipeline, we construct ManiTwin-100K, a dataset containing 100K high-quality annotated 3D assets. Each asset is equipped with physical properties, language descriptions, functional annotations, and verified manipulation proposals. Experiments demonstrate that ManiTwin provides an efficient asset synthesis and annotation workflow, and that ManiTwin-100K offers high-quality and diverse assets for manipulation data generation, random scene synthesis, and VQA data generation, establishing a strong foundation for scalable simulation data synthesis and policy learning. Our webpage is available at https://manitwin.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16866 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K Wang, Kaixuan Chen, Tianxing Liu, Jiawei Su, Honghao Zhu, Shaolong Wang, Minxuan Li, Zixuan Chen, Yue Gao, Huan-ang Qin, Yusen Wang, Jiawei Zhang, Qixuan Xu, Lan Yu, Jingyi Mu, Yao Luo, Ping Robotics Artificial Intelligence Graphics Machine Learning Software Engineering Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital assets, in both scale and diversity. In this work, we present ManiTwin, an automated and efficient pipeline for generating data-generation-ready digital object twins. Our pipeline transforms a single image into simulation-ready and semantically annotated 3D asset, enabling large-scale robotic manipulation data generation. Using this pipeline, we construct ManiTwin-100K, a dataset containing 100K high-quality annotated 3D assets. Each asset is equipped with physical properties, language descriptions, functional annotations, and verified manipulation proposals. Experiments demonstrate that ManiTwin provides an efficient asset synthesis and annotation workflow, and that ManiTwin-100K offers high-quality and diverse assets for manipulation data generation, random scene synthesis, and VQA data generation, establishing a strong foundation for scalable simulation data synthesis and policy learning. Our webpage is available at https://manitwin.github.io/. |
| title | ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K |
| topic | Robotics Artificial Intelligence Graphics Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2603.16866 |