Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with
  Hybrid History Condition
Recent advances in diffusion-based and controllable video generation have
enabled high-quality and temporally coherent video synthesis, laying the
groundwork for immersive interactive gaming experiences. However, current
methods face limitations in dynamics, generality, long-term consistency, and
efficiency, which limit the ability to create various gameplay videos. To
address these gaps, we introduce Hunyuan-GameCraft, a novel framework for
high-dynamic interactive video generation in game environments. To achieve
fine-grained action control, we unify standard keyboard and mouse inputs into a
shared camera representation space, facilitating smooth interpolation between
various camera and movement operations. Then we propose a hybrid
history-conditioned training strategy that extends video sequences
autoregressively while preserving game scene information. Additionally, to
enhance inference efficiency and playability, we achieve model distillation to
reduce computational overhead while maintaining consistency across long
temporal sequences, making it suitable for real-time deployment in complex
interactive environments. The model is trained on a large-scale dataset
comprising over one million gameplay recordings across over 100 AAA games,
ensuring broad coverage and diversity, then fine-tuned on a carefully annotated
synthetic dataset to enhance precision and control. The curated game scene data
significantly improves the visual fidelity, realism and action controllability.
Extensive experiments demonstrate that Hunyuan-GameCraft significantly
outperforms existing models, advancing the realism and playability of
interactive game video generation.
6858c46fc0c8e29df8ea3c8a
Jiaqi Li
6858c46fc0c8e29df8ea3c8b
Junshu Tang
admin_assigned
2025-06-24T18:13:52.753Z
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junshu tang
user
tangjs
6858c46fc0c8e29df8ea3c8c
Zhiyong Xu
admin_assigned
2025-06-24T18:13:46.662Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ktS3nDRZwIW04mAbqqmFY.png
Zhiyong Xu
user
TeddyXu2
6858c46fc0c8e29df8ea3c8d
Longhuang Wu
6858c46fc0c8e29df8ea3c8e
Yuan Zhou
6858c46fc0c8e29df8ea3c8f
Shuai Shao
admin_assigned
2025-06-24T18:14:02.786Z
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/avatars/c05e4a869de2c65f9e7db3c5b47c3776.svg
Shuai Shao
user
shuaishao93
6858c46fc0c8e29df8ea3c90
Tianbao Yu
6858c46fc0c8e29df8ea3c91
Zhiguo Cao
6858c46fc0c8e29df8ea3c92
Qinglin Lu
Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with
  Hybrid History Condition
Recent advances in diffusion-based and controllable video generation have
enabled high-quality and temporally coherent video synthesis, laying the
groundwork for immersive interactive gaming experiences. However, current
methods face limitations in dynamics, generality, long-term consistency, and
efficiency, which limit the ability to create various gameplay videos. To
address these gaps, we introduce Hunyuan-GameCraft, a novel framework for
high-dynamic interactive video generation in game environments. To achieve
fine-grained action control, we unify standard keyboard and mouse inputs into a
shared camera representation space, facilitating smooth interpolation between
various camera and movement operations. Then we propose a hybrid
history-conditioned training strategy that extends video sequences
autoregressively while preserving game scene information. Additionally, to
enhance inference efficiency and playability, we achieve model distillation to
reduce computational overhead while maintaining consistency across long
temporal sequences, making it suitable for real-time deployment in complex
interactive environments. The model is trained on a large-scale dataset
comprising over one million gameplay recordings across over 100 AAA games,
ensuring broad coverage and diversity, then fine-tuned on a carefully annotated
synthetic dataset to enhance precision and control. The curated game scene data
significantly improves the visual fidelity, realism and action controllability.
Extensive experiments demonstrate that Hunyuan-GameCraft significantly
outperforms existing models, advancing the realism and playability of
interactive game video generation.
Hunyuan-GameCraft is a novel framework for high-dynamic interactive video generation in game environments that addresses limitations in dynamics, generality, and efficiency through unified input representation, hybrid history-conditioned training, and model distillation.
diffusion-based controllable video generation temporally coherent video synthesis high-dynamic interactive video generation shared camera representation space hybrid history-conditioned training strategy model distillation real-time deployment large-scale dataset synthetic dataset game scene data