SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion

• URL: http://arxiv.org/abs/2607.05392v1
• Date: Mon, 06 Jul 2026 17:59:35 GMT
• Title: SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion
• Abstract summary: We present SynCity 3000, a framework for generating 3D scenes globally coherent while enabling fine-grained layout control.<n>Building on the ability of current image-to-3D generators, we extend this capability to the scale of entire scenes by adapting the generator to be applicable as a convolutional operator.<n>We achieve this by fine-tuning the model on scene-like data generated by a new synthetic data engine.<n>The convolutional generator is then applied to a dimetric image of the entire scene, generated from the user prompt, resulting in 3D scenes of arbitrary size and complexity.
• Score: 80.07819974787664
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present SynCity 3000, a framework for generating 3D scenes that are globally coherent while enabling fine-grained layout control. Building on the ability of current image-to-3D generators to produce complex 3D assets from a single image, we extend this capability to the scale of entire scenes by adapting the generator to be applicable as a convolutional operator. We achieve this by fine-tuning the model on scene-like data generated by a new synthetic data engine, which we propose to address the scarcity of 3D scene data for training. The convolutional generator is then applied to a dimetric image of the entire scene, generated from the user prompt, resulting in 3D scenes of arbitrary size and complexity. Across diverse prompts and layouts, SynCity 3000 produces large, coherent, and detailed scenes, addressing the shortcomings of prior approaches to 3D scene generation.

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