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 ingredients. 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.

翻译：本文提出WorldPlay，一种流式视频扩散模型，能够实现具有长期几何一致性的实时交互式世界建模，解决了当前方法中速度与内存之间的权衡问题。WorldPlay的优势源于三个关键要素：1）我们采用双动作表征，以响应用户键盘和鼠标输入实现稳健的动作控制；2）为强制执行长期一致性，我们的重构上下文记忆机制动态地从过去帧重建上下文，并通过时间重框定使几何重要但时间久远的帧保持可访问性，有效缓解记忆衰减问题；3）我们还提出上下文强制（Context Forcing）这一针对记忆感知模型的新型蒸馏方法。通过对齐教师模型与学生模型的记忆上下文，该方法保持了学生模型使用长距离信息的能力，在实现实时速度的同时防止误差漂移。综合而言，WorldPlay能以24 FPS生成具有卓越一致性的长跨度720p流式视频，相较现有技术展现出显著优势，并在多样场景中呈现强泛化能力。项目主页与在线演示请见：https://3d-models.hunyuan.tencent.com/world/ 和 https://3d.hunyuan.tencent.com/sceneTo3D。