Hy-Nomas：面向自由形式交互式世界生成的视频世界模型
Hy-Nomas: Towards Free-form Interactive World Generation
Hy-Nomas 是一个 8B 参数的扩散模型，支持自由形式文本指令和跨多轮交互的相机控制，通过因果注意力与结构化视频描述相结合的方式遵循多轮文本和相机指令，在人类评估中优于现有世界模型，已开源模型权重、代码和基准。
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Hy-Nomas：面向自由形式交互式世界生成的视频世界模型
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Published on Oct 27
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Abstract
Hy-Nomas is a video world model that uses causal attention and structured captions to follow multi-turn text and camera instructions while preserving interaction history.
Interactive world generation requires models to follow successive commands while preserving relevant history and action consequences. We present Hy-Nomas, a video world model with an 8B diffusion backbone that supports free-form text instructions and continuous camera control across multiple interaction turns. It generates variable-length segments conditioned on current inputs and cached history. Our central design aligns structured video captions with action-aware causal attention: descriptive context remains accessible across segments, while action instructions are restricted to their intended temporal windows. A vision-language Prompt Enhancer (PE) translates user requests and multimodal interaction histories into structured conditions for the next segment. A multi-stage curriculum progressively introduces causal generation, text interaction, camera control, and training on model-generated histories, followed by diffusion distillation. In human evaluation, Hy-Nomas shows superior performance over existing world models. We release model weights, code and benchmarks, together with a lightweight adaptation workflow, to support reproducible research on interactive world generation.
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