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