Convert a 2D image to a 3D model

    Updated September 1, 2026 · Published September 1, 2026

    Converting a 2D image to a 3D model means answering a question the image never shows: what does the back look like? PolyMedium answers it with AI. Your image is restaged into a clean reference, the opposite side is synthesised, and a reconstruction engine builds a complete textured mesh from both views. One 2D image in, a real 3D model out, in about a minute, from $0.25.

    Real output: flat 2D drawing in, complete 3D model out.

    How it works

    1. 1

      Upload the 2D image

      Photo, screenshot, concept art, or drawing. The subject type is detected automatically and each queued image shows its price before you commit.

    2. 2

      The conversion runs

      Restage, background removal, opposite-side synthesis, reconstruction. We tested 41 engines and route each subject to the one that wins on it, so there is nothing to configure.

    3. 3

      Use the 3D model

      GLB with PBR textures for Unity, Unreal, Godot, Blender, and Roblox; USDZ for AR; STL for printing. All exports free.

    Why PolyMedium fits

    • • Full 3D reconstruction, not extrusion or a depth-map trick
    • • The back of the model is built, not guessed
    • • Objects and drawings $0.25; characters from $0.50
    • • Free GLB, USDZ, STL, and GIF exports

    Try it free

    $3.00 starter balance: twelve $0.25 models, no card required.

    FAQ

    How does 2D to 3D conversion actually work?

    The image is cleaned and restaged, the side the camera never saw is synthesised by an image model, and a 3D reconstruction engine builds the mesh from the resulting views. The texture comes baked as PBR maps that match the reference.

    Can it convert drawings and sketches, not just photos?

    Yes, and drawings get a dedicated pipeline that keeps the hand-drawn look instead of correcting it. A crayon dragon stays a crayon dragon.

    How accurate is the result?

    Faithful to what the image shows, with the hidden side reconstructed plausibly. Clear single subjects come out best; images with no single subject, like landscapes, are the one input that fails.

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