tencent / HY-WorldPlay

🎮 HY-World 1.5: A Systematic Framework for Interactive World Modeling with Real-Time Latency and Geometric Consistency

📖 Introduction

While HY-World 1.0 is capable of generating immersive 3D worlds, it relies on a lengthy offline generation process and lacks real-time interaction. HY-World 1.5 bridges this gap with 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. Our model draws power from four key designs. 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 design WorldCompass, a novel Reinforcement Learning (RL) post-training framework designed to directly improve the action-following and visual quality of the long-horizon, autoregressive video model. 4) We also propose Context Forcing, a novel distillation method designed for memory-aware models. 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, HY-World 1.5 generates long-horizon streaming video at 24 FPS with superior consistency, comparing favorably with existing techniques. Our model shows strong generalization across diverse scenes, supporting first-person and third-person perspectives in both real-world and stylized environments, enabling versatile applications such as 3D reconstruction, promptable events, and infinite world extension.

• Systematic Overview

  HY-World 1.5 has open-sourced a systematic and comprehensive training framework for real-time world models, covering the entire pipeline and all stages, including data, training, and inference deployment. The technical report discloses detailed training specifics for model pre-training, middle-training, reinforcement learning post-training, and memory-aware model distillation. In addition, the report introduces a series of engineering techniques aimed at reducing network transmission latency and model inference latency, thereby achieving a real-time streaming inference experience for users.

• Inference Pipeline

  Given a single image or text prompt to describe a world, our model performs a next chunk (16 video frames) prediction task to generate future videos conditioned on action from users. For the generation of each chunk, we dynamically reconstitute context memory from past chunks to enforce long-term temporal and geometric consistency.

📚 Citation

@article{hyworld2025,
  title={HY-World 1.5: A Systematic Framework for Interactive World Modeling with Real-Time Latency and Geometric Consistency},
  author={Team HunyuanWorld},
  journal={arXiv preprint},
  year={2025}
}

🙏 Acknowledgements

We would like to thank HunyuanWorld , HunyuanWorld-Mirror , HunyuanVideo , and FastVideo for their great work.