Yume-1.5: A Text-Controlled Interactive World Generation Model
Recent approaches have demonstrated the promise of using diffusion models to generate interactive and explorable worlds. However, most of these methods face critical challenges such as excessively large parameter sizes, reliance on lengthy inference steps, and rapidly growing historical context, which severely limit real-time performance and lack text-controlled generation capabilities. To address these challenges, we propose \method, a novel framework designed to generate realistic, interactive, and continuous worlds from a single image or text prompt. \method achieves this through a carefully designed framework that supports keyboard-based exploration of the generated worlds. The framework comprises three core components: (1) a long-video generation framework integrating unified context compression with linear attention; (2) a real-time streaming acceleration strategy powered by bidirectional attention distillation and an enhanced text embedding scheme; (3) a text-controlled method for generating world events. We have provided the codebase in the supplementary material.
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Xiaofeng Mao
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Zhen Li
claimed_verified
2025-12-31T20:57:31.400Z
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Zhen Li
Lixsp11
user
Lixsp11
695206a8746a34b55dd548df
Chuanhao Li
claimed_verified
2026-03-27T11:46:44.115Z
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Chuanhao
ChuanhaoLi
user
ChuanhaoLi
695206a8746a34b55dd548e0
Xiaojie Xu
695206a8746a34b55dd548e1
Kaining Ying
695206a8746a34b55dd548e2
Tong He
695206a8746a34b55dd548e3
Jiangmiao Pang
695206a8746a34b55dd548e4
Yu Qiao
695206a8746a34b55dd548e5
Kaipeng Zhang
claimed_verified
2026-03-25T10:52:22.307Z
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kaipeng
kpzhang996
user
kpzhang996
Yume-1.5: A Text-Controlled Interactive World Generation Model
Recent approaches have demonstrated the promise of using diffusion models to generate interactive and explorable worlds. However, most of these methods face critical challenges such as excessively large parameter sizes, reliance on lengthy inference steps, and rapidly growing historical context, which severely limit real-time performance and lack text-controlled generation capabilities. To address these challenges, we propose \method, a novel framework designed to generate realistic, interactive, and continuous worlds from a single image or text prompt. \method achieves this through a carefully designed framework that supports keyboard-based exploration of the generated worlds. The framework comprises three core components: (1) a long-video generation framework integrating unified context compression with linear attention; (2) a real-time streaming acceleration strategy powered by bidirectional attention distillation and an enhanced text embedding scheme; (3) a text-controlled method for generating world events. We have provided the codebase in the supplementary material.