Generative world models and 3D Gaussian Splatting promise to turn ordinary video into explorable 3D scenes, but little is reported on how they behave on uncurated, in-the-wild footage. We study what these models recover when the only input is a publicly available news walkthrough of a food bank warehouse, with presenters walking through every shot and no drawings, scans or site visit, and we use the result as the geometry of a digital twin. A commercial world model given 20 frames recovered camera poses and metric depth for three areas, which we fused into about 747,000 Gaussian splats for 200 credits (20 US cents). Splat models optimized locally on a CPU, at no credit cost, were photorealistic near the filmed path but degraded quickly away from it. On held-out frames, scored in pixels not covered by people, they reached 21.2 and 23.1 dB PSNR from 64 and 43 training frames, and lost 3.8 and 5.4 dB when given only the 8 frames a world model accepts. Removing the trainer's background fill and person-hull carving raised held-out PSNR, showing that cleaning the space behind moving people costs fidelity in observed pixels while its gain lies where no ground truth exists. Asking the world model to generate views of areas the video never showed failed on the provider's side twice, so extrapolation could not be evaluated and unseen space was filled by a scripted model instead. We also describe the corrections needed to turn world-model output into a valid, metric Universal Scene Description (OpenUSD) asset with its native splat schema, which serves as the only interface to a data layer, a browser viewer, Blender and NVIDIA Omniverse. The findings suggest that today's models recover the observed parts of a real scene cheaply and at metric scale, while few-view fidelity and extrapolating unobserved space remain open. The work is an exploration of these methods, not a deployed product.

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