Matrix-Game: Interactive World Foundation Model
We introduce Matrix-Game, an interactive world foundation model for
controllable game world generation. Matrix-Game is trained using a two-stage
pipeline that first performs large-scale unlabeled pretraining for environment
understanding, followed by action-labeled training for interactive video
generation. To support this, we curate Matrix-Game-MC, a comprehensive
Minecraft dataset comprising over 2,700 hours of unlabeled gameplay video clips
and over 1,000 hours of high-quality labeled clips with fine-grained keyboard
and mouse action annotations. Our model adopts a controllable image-to-world
generation paradigm, conditioned on a reference image, motion context, and user
actions. With over 17 billion parameters, Matrix-Game enables precise control
over character actions and camera movements, while maintaining high visual
quality and temporal coherence. To evaluate performance, we develop GameWorld
Score, a unified benchmark measuring visual quality, temporal quality, action
controllability, and physical rule understanding for Minecraft world
generation. Extensive experiments show that Matrix-Game consistently
outperforms prior open-source Minecraft world models (including Oasis and
MineWorld) across all metrics, with particularly strong gains in
controllability and physical consistency. Double-blind human evaluations
further confirm the superiority of Matrix-Game, highlighting its ability to
generate perceptually realistic and precisely controllable videos across
diverse game scenarios. To facilitate future research on interactive
image-to-world generation, we will open-source the Matrix-Game model weights
and the GameWorld Score benchmark at https://github.com/SkyworkAI/Matrix-Game.
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Yifan Zhang
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2025-06-25T08:09:21.431Z
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Yifan Zhang
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Vanint
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Chunli Peng
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2025-06-25T13:32:40.141Z
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CHUNLI PENG
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Boyang Wang
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Boyang Wang
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Puyi Wang
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Puyi Wang
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Qingcheng Zhu
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Fei Kang
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2025-08-13T07:30:28.722Z
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老k
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kangfei
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Biao Jiang
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Zedong Gao
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Eric Li
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Yang Liu
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Yahui Zhou
Matrix-Game: Interactive World Foundation Model
We introduce Matrix-Game, an interactive world foundation model for
controllable game world generation. Matrix-Game is trained using a two-stage
pipeline that first performs large-scale unlabeled pretraining for environment
understanding, followed by action-labeled training for interactive video
generation. To support this, we curate Matrix-Game-MC, a comprehensive
Minecraft dataset comprising over 2,700 hours of unlabeled gameplay video clips
and over 1,000 hours of high-quality labeled clips with fine-grained keyboard
and mouse action annotations. Our model adopts a controllable image-to-world
generation paradigm, conditioned on a reference image, motion context, and user
actions. With over 17 billion parameters, Matrix-Game enables precise control
over character actions and camera movements, while maintaining high visual
quality and temporal coherence. To evaluate performance, we develop GameWorld
Score, a unified benchmark measuring visual quality, temporal quality, action
controllability, and physical rule understanding for Minecraft world
generation. Extensive experiments show that Matrix-Game consistently
outperforms prior open-source Minecraft world models (including Oasis and
MineWorld) across all metrics, with particularly strong gains in
controllability and physical consistency. Double-blind human evaluations
further confirm the superiority of Matrix-Game, highlighting its ability to
generate perceptually realistic and precisely controllable videos across
diverse game scenarios. To facilitate future research on interactive
image-to-world generation, we will open-source the Matrix-Game model weights
and the GameWorld Score benchmark at https://github.com/SkyworkAI/Matrix-Game.
Matrix-Game, a controllable game world generation model trained in a two-stage process, outperforms existing models by producing high-quality, action-controllable, and physically consistent Minecraft world videos.
Matrix-Game interactive world foundation model large-scale unlabeled pretraining action-labeled training contrrollable image-to-world generation Matrix-Game-MC motion context character actions camera movements visual quality temporal coherence GameWorld Score double-blind human evaluations interactive image-to-world generation Oasis MineWorld perceptually realistic