[2606.16993] DreamX-World 1.0: A General-Purpose Interactive World Model

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2606.16993 (cs)

[Submitted on 15 Jun 2026]

Title:DreamX-World 1.0: A General-Purpose Interactive World Model

Authors:DreamX Team, Yancheng Bai, Rui Chen, Xiangxiang Chu, Rujing Dang, Hao Dou, Bingjie Gao, Qiwen Gu, Siyu Hong, Jiachen Lei, Geng Li, Jifan Li, Ruimin Lin, Qingfeng Shi, Bingze Song, Lei Sun, Jing Tang, Ruitian Tian, Jun Wang, Jiahong Wu, Pengfei Zhang, Shen Zhang, Jiashu Zhu<br>View a PDF of the paper titled DreamX-World 1.0: A General-Purpose Interactive World Model, by DreamX Team and 22 other authors

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Abstract:DreamX-World 1.0 is a general-purpose interactive text/image-to-video world model for controllable long-horizon generation. It supports camera navigation, revisits to previously observed regions, and promptable events across photorealistic, game-style, and stylized domains. Our data engine combines camera-accurate Unreal Engine rendering, action-rich gameplay recordings, and real-world videos with recovered camera geometry. For camera control, we introduce E-PRoPE, a lightweight variant of projective positional encoding that retains PRoPE's projective camera geometry while applying camera-aware attention to spatially reduced tokens. We convert a bidirectional video generator into a few-step autoregressive world model using causal forcing, DMD-style distillation, and long-rollout training. Training on self-generated long-horizon contexts exposes the model to its own generated history and reduces the style and color drift that accumulates across autoregressive chunks. Memory-Conditioned Scene Persistence retrieves earlier views through camera-geometry-based retrieval, while residual recycling makes the conditioning path less sensitive to imperfect memory latents. Event Instruction Tuning adds composable event control, and reinforcement learning alignment recovers camera control and visual quality after distillation. With mixed-precision DiT execution, residual reuse, 75\%-pruned VAE decoding, and asynchronous pipeline parallelism, DreamX-World 1.0 reaches up to 16\,FPS on eight RTX\,5090 GPUs. On our 5-second basic evaluation, DreamX-World 1.0 achieves a camera-control score of 73.75 and an overall score of 84.76, outperforming HY-WorldPlay 1.5 and LingBot-World in overall score, which achieve 80.79 and 80.45, respectively.

Comments:<br>Project page: this https URL, Code: this https URL

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Computer Vision and Pattern Recognition (cs.CV)

Cite as:<br>arXiv:2606.16993 [cs.CV]

(or<br>arXiv:2606.16993v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.16993

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Rui Chen [view email]<br>[v1]<br>Mon, 15 Jun 2026 17:30:33 UTC (11,504 KB)

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