SHARP demo: explore the results

Explore the official comparison gallery

This gallery contains pre-rendered research examples with interactive scene and method comparisons. It does not upload your photo or run SHARP inference in your browser.

Generate a scene from your own image

Install SHARP

conda create -n sharp python=3.13 -y
conda activate sharp
git clone https://github.com/apple-aiml-research/ml-sharp.git
cd ml-sharp
python -m pip install -r requirements.txt
sharp --help

Put one or more photos in an input directory, then run prediction. The checkpoint downloads automatically on the first run.

sharp predict -i ./input -o ./output

Successful prediction writes 3D Gaussian .ply files to the output directory. Open them in a compatible Gaussian-splat viewer. The result supports nearby viewpoints; it is not a complete reconstruction of unseen parts of a scene.

Choose the right device

Gaussian prediction

CPU, CUDA or Apple MPS

Render a camera-path video

CUDA GPU required by the official renderer

Optional: render a video on CUDA

sharp predict -i ./input -o ./output --render
# Or render previously generated Gaussians:
sharp render -i ./output -o ./renderings

If something goes wrong

• First run appears slow: allow time for checkpoint download and initialization; inspect the terminal output.
• Video rendering fails on Mac: prediction supports MPS, but the official --render path requires CUDA.
• Scene orientation looks wrong in another viewer: SHARP uses OpenCV coordinates; check scale and rotation in that viewer.

Official code and examples · checked September 12, 2026