ml-sharp README

The project's README from github.com/apple-aiml-research/ml-sharp. As one markdown file: README.md.

Sharp Monocular View Synthesis in Less Than a Second

Project Page arXiv

This software project accompanies the research paper: Sharp Monocular View Synthesis in Less Than a Second by Lars Mescheder, Wei Dong, Shiwei Li, Xuyang Bai, Marcel Santos, Peiyun Hu, Bruno Lecouat, Mingmin Zhen, Amaël Delaunoy, Tian Fang, Yanghai Tsin, Stephan Richter and Vladlen Koltun.

We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the depicted scene. This is done in less than a second on a standard GPU via a single feedforward pass through a neural network. The 3D Gaussian representation produced by SHARP can then be rendered in real time, yielding high-resolution photorealistic images for nearby views. The representation is metric, with absolute scale, supporting metric camera movements. Experimental results demonstrate that SHARP delivers robust zero-shot generalization across datasets. It sets a new state of the art on multiple datasets, reducing LPIPS by 25–34% and DISTS by 21–43% versus the best prior model, while lowering the synthesis time by three orders of magnitude.

Getting started

We recommend to first create a python environment:

conda create -n sharp python=3.13

Afterwards, you can install the project using

pip install -r requirements.txt

To test the installation, run

sharp --help

Using the CLI

To run prediction:

sharp predict -i /path/to/input/images -o /path/to/output/gaussians

The model checkpoint will be downloaded automatically on first run and cached locally at ~/.cache/torch/hub/checkpoints/.

Alternatively, you can download the model directly:

wget https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt

To use a manually downloaded checkpoint, specify it with the -c flag:

sharp predict -i /path/to/input/images -o /path/to/output/gaussians -c sharp_2572gikvuh.pt

The results will be 3D gaussian splats (3DGS) in the output folder. The 3DGS .ply files are compatible to various public 3DGS renderers. We follow the OpenCV coordinate convention (x right, y down, z forward). The 3DGS scene center is roughly at (0, 0, +z). When dealing with 3rdparty renderers, please scale and rotate to re-center the scene accordingly.

Rendering trajectories (CUDA GPU only)

Additionally you can render videos with a camera trajectory. While the gaussians prediction works for all CPU, CUDA, and MPS, rendering videos via the --render option currently requires a CUDA GPU. The gsplat renderer takes a while to initialize at the first launch.

sharp predict -i /path/to/input/images -o /path/to/output/gaussians --render

# Or from the intermediate gaussians:
sharp render -i /path/to/output/gaussians -o /path/to/output/renderings

Evaluation

Please refer to the paper for both quantitative and qualitative evaluations. Additionally, please check out this qualitative examples page containing several video comparisons against related work.

Citation

If you find our work useful, please cite the following paper:

@inproceedings{Sharp2025:arxiv,
  title      = {Sharp Monocular View Synthesis in Less Than a Second},
  author     = {Lars Mescheder and Wei Dong and Shiwei Li and Xuyang Bai and Marcel Santos and Peiyun Hu and Bruno Lecouat and Mingmin Zhen and Ama\"{e}l Delaunoy and Tian Fang and Yanghai Tsin and Stephan R. Richter and Vladlen Koltun},
  journal    = {arXiv preprint arXiv:2512.10685},
  year       = {2025},
  url        = {https://arxiv.org/abs/2512.10685},
}

Acknowledgements

Our codebase is built using multiple opensource contributions, please see ACKNOWLEDGEMENTS for more details.

License

Please check out the repository LICENSE before using the provided code and LICENSE_MODEL for the released models.

ml-sharp (apple-aiml-research/ml-sharp)

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9K ·GitHub
Ml-sharp
What is this?SHARP (ml-sharp) is the research code for turning a single photograph into a photorealistic 3D scene: it regresses a 3D Gaussian Splatting representation of the scene in a single feedforward pass, in under a second on a standard GPU, and that representation can then be rendered in real time from nearby viewpoints.
Is this a ready-made app or research code with a CLI?It's research code accompanying an academic paper, exposed as a command-line tool for running predictions, not a packaged end-user app.
How do I get it running?Create a Python environment, install the project, and run the included test to verify the setup; the pretrained model checkpoint is then downloaded automatically on first run and cached locally.
Do I need a CUDA GPU to use this?Gaussian prediction from an image runs on CPU, CUDA, or Apple MPS, but rendering a camera-trajectory video with the --render option currently only works on a CUDA GPU.
Any gotchas when using the --render video option?Besides being CUDA-only, the underlying gsplat renderer takes noticeably longer to initialize on its very first launch.
Can I freely use the code and the released models?Code and models are covered by separate license files, so you should check both before using anything from the repo.
How fast and accurate is SHARP compared to prior methods?SHARP sets a new state of the art, cutting LPIPS by 25-34% and DISTS by 21-43% versus the best prior model, while lowering synthesis time by three orders of magnitude.
What format do I get the 3D scene in?Output is a 3D Gaussian Splatting .ply file saved to the output folder, compatible with common public 3DGS renderers and using the OpenCV camera coordinate convention.
Is the generated scene at real-world scale?Yes, the 3D Gaussian representation is metric with absolute scale, so it supports real, metric camera movements rather than an arbitrary scale.
What are similar projects?nerfstudio, vggt, instant-ngp, InsertAny3D, WorldSculpt, vggt-omega, gaussian-splatting, sam-3d-objects
Which topics is it in?real-time-rendering, gaussian-splatting, image-to-3d, neural-rendering, 3d-reconstruction, paper, 3d, academic-research, inference, nvidia, computer-vision, cuda
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apple-aiml-research
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CommitSep 11
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