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We present Worldsheet, a method for novel view synthesis using just a singleRGB image as input. The main insight is that simply shrink-wrapping a planarmesh sheet onto the input image, consistent with the learned intermediatedepth, captures underlying geometry sufficient to generate photorealisticunseen views with large viewpoint changes. To operationalize this, we propose anovel differentiable texture sampler that allows our wrapped mesh sheet to betextured and rendered differentiably into an image from a target viewpoint. Ourapproach is category-agnostic, end-to-end trainable without using any 3Dsupervision, and requires a single image at test time. We also explore a simpleextension by stacking multiple layers of Worldsheets to better handleocclusions. Worldsheet consistently outperforms prior state-of-the-art methodson single-image view synthesis across several datasets. Furthermore, thissimple idea captures novel views surprisingly well on a wide range ofhigh-resolution in-the-wild images, converting them into navigable 3D pop-ups.Video results and code are available at https://worldsheet.github.io.

Worldsheet: Wrapping the World in a 3D Sheet for View Synthesis from a Single Image