Dreamland: Controllable World Creation with Simulator and Generative Models

• URL: http://arxiv.org/abs/2506.08006v1
• Date: Mon, 09 Jun 2025 17:59:52 GMT
• Title: Dreamland: Controllable World Creation with Simulator and Generative Models
• Authors: Sicheng Mo, Ziyang Leng, Leon Liu, Weizhen Wang, Honglin He, Bolei Zhou,
• Abstract summary: Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation.<n>But they often lack element-wise controllability, hindering their use in editing scenes and training embodied AI agents.<n>We propose Dreamland, a hybrid world generation framework combining the granular control of a physics-based simulator and the photorealistic content output of large-scale pretrained generative models.
• Score: 32.427050300421115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation, but they often lack element-wise controllability, hindering their use in editing scenes and training embodied AI agents. We propose Dreamland, a hybrid world generation framework combining the granular control of a physics-based simulator and the photorealistic content output of large-scale pretrained generative models. In particular, we design a layered world abstraction that encodes both pixel-level and object-level semantics and geometry as an intermediate representation to bridge the simulator and the generative model. This approach enhances controllability, minimizes adaptation cost through early alignment with real-world distributions, and supports off-the-shelf use of existing and future pretrained generative models. We further construct a D3Sim dataset to facilitate the training and evaluation of hybrid generation pipelines. Experiments demonstrate that Dreamland outperforms existing baselines with 50.8% improved image quality, 17.9% stronger controllability, and has great potential to enhance embodied agent training. Code and data will be made available.

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