WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914204446883840 |
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| author | Sun, Wenqiang Zhang, Haiyu Wang, Haoyuan Wu, Junta Wang, Zehan Wang, Zhenwei Wang, Yunhong Zhang, Jun Wang, Tengfei Guo, Chunchao |
| author_facet | Sun, Wenqiang Zhang, Haiyu Wang, Haoyuan Wu, Junta Wang, Zehan Wang, Zhenwei Wang, Yunhong Zhang, Jun Wang, Tengfei Guo, Chunchao |
| contents | This paper presents WorldPlay, a streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency, resolving the trade-off between speed and memory that limits current methods. WorldPlay draws power from three key innovations. 1) We use a Dual Action Representation to enable robust action control in response to the user's keyboard and mouse inputs. 2) To enforce long-term consistency, our Reconstituted Context Memory dynamically rebuilds context from past frames and uses temporal reframing to keep geometrically important but long-past frames accessible, effectively alleviating memory attenuation. 3) We also propose Context Forcing, a novel distillation method designed for memory-aware model. Aligning memory context between the teacher and student preserves the student's capacity to use long-range information, enabling real-time speeds while preventing error drift. Taken together, WorldPlay generates long-horizon streaming 720p video at 24 FPS with superior consistency, comparing favorably with existing techniques and showing strong generalization across diverse scenes. Project page and online demo can be found: https://3d-models.hunyuan.tencent.com/world/ and https://3d.hunyuan.tencent.com/sceneTo3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling Sun, Wenqiang Zhang, Haiyu Wang, Haoyuan Wu, Junta Wang, Zehan Wang, Zhenwei Wang, Yunhong Zhang, Jun Wang, Tengfei Guo, Chunchao Computer Vision and Pattern Recognition Graphics This paper presents WorldPlay, a streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency, resolving the trade-off between speed and memory that limits current methods. WorldPlay draws power from three key innovations. 1) We use a Dual Action Representation to enable robust action control in response to the user's keyboard and mouse inputs. 2) To enforce long-term consistency, our Reconstituted Context Memory dynamically rebuilds context from past frames and uses temporal reframing to keep geometrically important but long-past frames accessible, effectively alleviating memory attenuation. 3) We also propose Context Forcing, a novel distillation method designed for memory-aware model. Aligning memory context between the teacher and student preserves the student's capacity to use long-range information, enabling real-time speeds while preventing error drift. Taken together, WorldPlay generates long-horizon streaming 720p video at 24 FPS with superior consistency, comparing favorably with existing techniques and showing strong generalization across diverse scenes. Project page and online demo can be found: https://3d-models.hunyuan.tencent.com/world/ and https://3d.hunyuan.tencent.com/sceneTo3D. |
| title | WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2512.14614 |