Title: Dreamland: Hybrid World Creation with Simulator and Generative Models
Primary area: generative models
Keywords: Computer Vision, Generative Models, World Model

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 that combines the granular control of a physics-based simulator with 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 the 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.

TL;DR: Dreamland is a hybrid world‐generation framework that uses a layered world abstraction to integrate physics‐based simulation with pretrained generative models, enabling fine‐grained, element‐wise control and photorealistic image/video synthesis.