World2Minecraft: Occupancy-Driven Simulated Scenes Construction
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911635783811072 |
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| author | Zhang, Lechao Xu, Haoran Gong, Jingyu Wang, Xuhong Xie, Yuan Tan, Xin |
| author_facet | Zhang, Lechao Xu, Haoran Gong, Jingyu Wang, Xuhong Xie, Yuan Tan, Xin |
| contents | Embodied intelligence requires high-fidelity simulation environments to support perception and decision-making, yet existing platforms often suffer from data contamination and limited flexibility. To mitigate this, we propose World2Minecraft to convert real-world scenes into structured Minecraft environments based on 3D semantic occupancy prediction. In the reconstructed scenes, we can effortlessly perform downstream tasks such as Vision-Language Navigation(VLN). However, we observe that reconstruction quality heavily depends on accurate occupancy prediction, which remains limited by data scarcity and poor generalization in existing models. We introduce a low-cost, automated, and scalable data acquisition pipeline for creating customized occupancy datasets, and demonstrate its effectiveness through MinecraftOcc, a large-scale dataset featuring 100,165 images from 156 richly detailed indoor scenes. Extensive experiments show that our dataset provides a critical complement to existing datasets and poses a significant challenge to current SOTA methods. These findings contribute to improving occupancy prediction and highlight the value of World2Minecraft in providing a customizable and editable platform for personalized embodied AI research. Project page:https://world2minecraft.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27578 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | World2Minecraft: Occupancy-Driven Simulated Scenes Construction Zhang, Lechao Xu, Haoran Gong, Jingyu Wang, Xuhong Xie, Yuan Tan, Xin Computer Vision and Pattern Recognition Embodied intelligence requires high-fidelity simulation environments to support perception and decision-making, yet existing platforms often suffer from data contamination and limited flexibility. To mitigate this, we propose World2Minecraft to convert real-world scenes into structured Minecraft environments based on 3D semantic occupancy prediction. In the reconstructed scenes, we can effortlessly perform downstream tasks such as Vision-Language Navigation(VLN). However, we observe that reconstruction quality heavily depends on accurate occupancy prediction, which remains limited by data scarcity and poor generalization in existing models. We introduce a low-cost, automated, and scalable data acquisition pipeline for creating customized occupancy datasets, and demonstrate its effectiveness through MinecraftOcc, a large-scale dataset featuring 100,165 images from 156 richly detailed indoor scenes. Extensive experiments show that our dataset provides a critical complement to existing datasets and poses a significant challenge to current SOTA methods. These findings contribute to improving occupancy prediction and highlight the value of World2Minecraft in providing a customizable and editable platform for personalized embodied AI research. Project page:https://world2minecraft.github.io/. |
| title | World2Minecraft: Occupancy-Driven Simulated Scenes Construction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.27578 |