What is SHARP?
SHARP is Apple's open-source AI model that can convert a single 2D photo into a realistic 3D scene in less than 1 second. The model uses 3D Gaussian representation technology, using a single feedforward pass of a neural network to quickly predict the 3D structure and details of a scene. Compared with traditional methods, SHARP improves synthesis speed by three orders of magnitude, while performing excellently on multiple benchmarks, reducing LPIPS by 25% – 34% and DISTS by 21% – 43%. The generated 3D views are closer to the real world in detail and structure.
SHARP's Main Features
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Fast View Synthesis: Completes the generation of a 3D scene in less than 1 second through a single neural network feedforward pass.
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High-Resolution Rendering: Supports real-time rendering of high-resolution 3D views, with rich details and realistic structure.
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Absolute Scale Support: The 3D scenes generated by the model have absolute scale, supporting realistic camera motion simulation.
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Zero-Shot Generalization: Performs excellently on multiple datasets, can generalize to unseen scenes, and has good robustness.
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Efficiency and High Quality: Compared with traditional methods, synthesis speed is improved by three orders of magnitude, while image quality is significantly better than existing technologies.
SHARP's Technical Principles
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3D Gaussian Representation：Represents objects and structures in a scene as Gaussian distributions (Gaussian spheres) with color, position, and shape information.This representation method can efficiently capture the geometric and appearance information of a scene while supporting efficient rendering.
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Neural Network Regression：Uses a deep neural network to regress the parameters of the scene's 3D Gaussian representation from a single 2D photo.The network is trained on massive synthetic data and real-world data, learning general depth and geometric patterns.
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Fast Modeling and Rendering：When processing a new photo, the model quickly predicts the positions, sizes, and appearances of millions of Gaussian distributions through a single feedforward pass.The generated 3D scene supports real-time rendering and can run on standard GPUs at over 100 frames per second.
SHARP's Project Address
- Project Website：https://apple.github.io/ml-sharp/
- GitHub Repository：https://github.com/apple/ml-sharp
- arXiv Technical Paper：https://arxiv.org/pdf/2512.10685
SHARP's Application Scenarios
- Virtual Scene Construction: By quickly converting a single photo into a 3D scene, SHARP can provide realistic virtual environments for VR and AR applications, enhancing the user experience.
- Film and Television Production: Quickly generates high-quality 3D scenes for special effects production in movies, TV series, and advertisements, saving time and cost.
- Environmental Modeling: Quickly generates 3D maps of the surrounding environment, helping autonomous vehicles and robots better understand scenes, perform path planning, and navigate.
- Cultural Relic Digitization: Quickly generates 3D models of cultural relics from a single photo, used for digital preservation and display of cultural heritage.
- Virtual Laboratory: Provides students with a virtual experimental environment, enhancing the learning experience through 3D scenes.