2D And 3D Face Alignment
2D & 3D facial landmark detection with pretrained models and LS3D-W dataset.
A solid research baseline for 2D/3D face alignment, but purely academic. Don't expect a plug-and-play API or commercial support. The dataset and pretrained models are invaluable for computer vision researchers, though.
- Computer vision researchers studying face alignment
- Developers building facial analysis prototypes requiring pretrained models
- Teams needing access to the LS3D-W 3D facial landmark dataset
- Students learning deep learning for landmark localization
- Beginners without deep learning experience
- Production applications needing a stable API or SaaS
- Real-time mobile deployment (no mobile-optimized code)
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In short
2D And 3D Face Alignment — 2D & 3D facial landmark detection with pretrained models and LS3D-W dataset. Best for Computer vision researchers studying face alignment, Developers building facial analysis prototypes requiring pretrained models, Teams needing access to the LS3D-W 3D facial landmark dataset. Free to use.
Viability Score
How likely is 2D And 3D Face Alignment to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- 2D facial landmark detection with 68 points
- 3D facial landmark detection with 68 points
- Pretrained 2D-FAN model for 2D alignment
- Pretrained 3D-FAN model for 3D alignment
- Pretrained 2D-to-3D-FAN model for converting 2D landmarks to 3D
- Pretrained 3D-FAN-depth model for depth estimation
- LS3D-W dataset with ~230,000 3D-annotated images
- Guide for converting 2D annotations to 3D using 2D-to-3D-FAN
- PyTorch and Torch7 implementations available
- Handles large pose, varying initialization, and resolution changes
About 2D And 3D Face Alignment
2D And 3D Face Alignment is a research-focused tool that implements the networks from the ICCV 2017 paper by Adrian Bulat and Georgios Tzimiropoulos. It provides state-of-the-art 2D and 3D facial landmark detection by combining a strong architecture for landmark localization with a residual block. The tool is designed for computer vision researchers and developers working on facial analysis, enabling accurate landmark detection even under large pose, varying initialization, and resolution changes. It includes the LS3D-W dataset, a large-scale 3D facial landmark dataset of ~230,000 images, and pretrained models (2D-FAN, 3D-FAN, 2D-to-3D-FAN, 3D-FAN-depth) for direct use. The dataset is available upon request by email. Compared to commercial face alignment APIs, this tool offers more control and transparency for academic use but lacks production support.
Behind the Verdict
If you're a computer vision researcher needing a strong baseline for 2D/3D facial landmark detection, this tool is a no-brainer. The pretrained models work remarkably well across challenging conditions. The LS3D-W dataset is a unique resource. But if you're a developer looking for a production-ready solution with an API, cloud deployment, or real-time mobile performance, look elsewhere. There's no official pipeline for deployment, no customer support, and the code is research-grade. For production, consider commercial APIs like Face++ or Azure Face API. For research, this is pure gold.
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Use Cases
- Run 2D facial landmark detection on in-the-wild images using the pretrained 2D-FAN model.
- Generate 3D facial landmarks from 2D annotations using the 2D-to-3D-FAN model.
- Train or fine-tune the provided models on custom datasets for improved accuracy.
- Utilize the LS3D-W dataset to benchmark new face alignment algorithms.
- Combine the models with face detection pipelines for end-to-end facial analysis.
Models Under the Hood
as of 2026-07-17
Limitations
- No API, no web interface, no prebuilt executables.
- The tool is code-only and requires PyTorch/Torch7 knowledge.
- No commercial support or documentation beyond the paper.
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