FaceLift

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FaceLift is an AI tool for single image 3D face reconstruction using Gaussian Splatting. It creates 3D facial models from 2D images, primarily for researchers and developers.

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At a glance

Pricing

free

Free tier

Yes

API

—

Skill level

Technical

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About

What is FaceLift?

FaceLift is an AI tool designed for single image 3D face reconstruction. It leverages Gaussian Splatting technology to generate three-dimensional facial models from a single two-dimensional input image. This tool is presented as a Hugging Face demo, associated with an ICCV 2025 paper, indicating its research-oriented nature. It is particularly useful for researchers and developers working in the field of computer graphics who need to create 3D facial models efficiently from limited input.

Best used for

Reconstructing 3D facial models from single 2D images for research and development in computer graphics.

Common actions

Reconstruct 3D faces

Generate 3D models

Research computer graphics

Develop facial applications

AI chatbotsContent generationTask automationAutomationEducationfun toolsai

Capabilities

Key features

Single image 3D reconstruction
Gaussian Splatting
Hugging Face demo
2D to 3D facial models

Target Audience

ResearchersDevelopersComputer Graphics Professionals

Integrations

Not yet documented

Pricing & Plans

free

Free

FAQs

Are there any usage limits or API rate limits for the Hugging Face demo of FaceLift?

As a Hugging Face demo, FaceLift may be subject to general platform usage policies or rate limits. For heavy or commercial use, it's advisable to check Hugging Face's specific terms for hosted demos or consider running the model locally if the code is made available.

Can FaceLift reconstruct non-human faces or objects, or is it strictly limited to human facial models?

FaceLift is specifically designed and optimized for human facial reconstruction, leveraging techniques like Gaussian Splatting for this purpose. It is not intended for reconstructing non-human faces or general 3D objects, and performance would likely be poor or non-existent for such inputs.

What kind of input image quality is required for optimal 3D reconstruction with FaceLift?

For optimal results, FaceLift generally requires clear, well-lit, and frontal or near-frontal input images of a single face. Images with occlusions, extreme angles, poor lighting, or low resolution may lead to less accurate or distorted 3D models.

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