2D And 3D Face Alignment
Free 2D/3D face alignment models plus the LS3D-W dataset of 230,000 images
A must-have for researchers and students who need a reliable 2D/3D face alignment baseline and the LS3D-W dataset. But it's not a product: no support, no API, and no mobile path. If you ship to production, look elsewhere. If you're building prototypes or reproducing results, this is the resource.
Verified 15d ago · liveness 59/100 · cite: rightaichoice.com/tools/2d-and-3d-face-alignment
- Computer vision researchers needing a strong 2D/3D face alignment baseline
- Developers building facial analysis prototypes with pretrained models
- Teams requiring the LS3D-W dataset for training or benchmarking
- Students learning deep learning for landmark localization
- Beginners without deep learning experience or code fluency
- Production applications needing a stable API or SaaS
- Real-time mobile deployment since no mobile-optimized code is provided
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Skip this if you need a production-ready face alignment API, mobile-optimized code, or commercial support — those are not provided.
$0 for everyone — models and dataset are free to download. No API keys or usage limits. You only pay in time and effort to set it up and adapt it. Compared to commercial APIs like Microsoft Face API or AWS Rekognition, this is unbeatable for research and prototyping.
In short
2D And 3D Face Alignment — Free 2D/3D face alignment models plus the LS3D-W dataset of 230,000 images. Best for Computer vision researchers needing a strong 2D/3D face alignment baseline, Developers building facial analysis prototypes with pretrained models, Teams requiring the LS3D-W dataset for training or benchmarking. Free to use.
What's new in 2D And 3D Face Alignment
Checked 6 days agoAcross the latest 1 update: 1 changelog entry.
What people actually say about 2D And 3D Face Alignment — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
11 mentions across 2 sources (GitHub, Lemmy) · researched Jul 6, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +State-of-the-art 2D and 3D facial landmark detection with 68 points.
- +Large-scale LS3D-W dataset with 230,000 annotated images for training.
- +Multiple pretrained models available: 2D-FAN, 3D-FAN, and mixed variants.
- +Supports conversion of 2D landmarks to 3D using 2D-to-3D-FAN model.
- +Based on peer-reviewed ICCV 2017 paper with solid methodology.
- −Reproducibility issues: users cannot match paper results.
- −Segmentation faults when running the main script.
- −Docker build fails due to missing Boost Python library.
- −Dlib face detector fails in some cases, blocking inference.
- −LS3D-W dataset license unclear, risky for commercial use.
- • Potential legal costs if dataset license violated
- • Engineering time to debug setup issues
Viability Score
How well maintained and how widely used is 2D And 3D Face Alignment? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- 2D facial landmark detection with 68 points
- 3D facial landmark detection with 68 points
- Pretrained 2D-FAN model
- Pretrained 3D-FAN model
- Pretrained 2D-to-3D-FAN model for converting 2D points to 3D
- Pretrained 3D-FAN-depth model
- LS3D-W dataset with ~230,000 3D-annotated images
- PyTorch implementation
- Torch7 implementation
- Handles large pose, varying initialization, and resolution changes
- Residual block architecture for landmark localization
- Dataset combines AFLW, 300VW, 300W, and FDDB annotations
- Free download with email request for dataset
About 2D And 3D Face Alignment
2D And 3D Face Alignment is a research project by Adrian Bulat and Georgios Tzimiropoulos, introduced at ICCV 2017. It asks how close deep networks are to solving face alignment and delivers four pretrained models—2D-FAN, 3D-FAN, 2D-to-3D-FAN, and 3D-FAN-depth—that locate 68 facial landmarks in both 2D and 3D. Built on a landmark-localization architecture combined with a residual block, these models handle large pose, varying initialization, and different resolutions. The project also introduces LS3D-W, a dataset of ~230,000 images with consistent 3D landmarks, constructed by converting 2D annotations from AFLW, 300VW, 300W, and FDDB using the 2D-to-3D-FAN model. As of July 2024, the entire dataset is fully released and available via email request. This project is aimed at computer vision researchers, students, and developers building facial analysis prototypes. You get PyTorch and Torch7 implementations, the models, and the dataset all for free—no API keys, no rate limits, no hidden costs. The trade-off: no production support, no APIs, and no mobile-optimized code. For academic work, reproducing results, or learning how modern face alignment works under the hood, this is an essential resource. For production apps, a commercial API might be safer.
Behind the Verdict
This is a research release, not a commercial product. The four pretrained models (2D-FAN, 3D-FAN, 2D-to-3D-FAN, 3D-FAN-depth) are solid baselines for face alignment, and the LS3D-W dataset is a notable resource for training and benchmarking — it's the largest of its kind at ~230,000 images. The main strengths are the free availability, the depth of the technical work (handling large pose, varying initialization, resolution), and the fact that the dataset is now fully released (as of July 2024). Weaknesses: there's no API, no web interface, no prebuilt executables, and no commercial support. You need to be comfortable with PyTorch or Torch7, and the documentation is limited to the research paper and the website. There's no mobile-optimized code, so real-time mobile deployment would require significant engineering. For beginners, the learning curve is steep. Where it fits: academic research, reproducing results, building prototypes, or teaching deep learning for vision. Where it doesn't: production applications that need a stable, supported face alignment service — for those, consider commercial APIs like Microsoft Face API, AWS Rekognition, or a hosted open-source solution.
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Real-world workflow fit
Concrete scenarios for the personas 2D And 3D Face Alignment actually fits — and what changes day-one when you adopt it.
Download the pretrained 2D-FAN model, test it on your own dataset, and compare against LS3D-W.
Outcome: You get a reproducible baseline for your research within a day.
Integrate 2D-FAN and 3D-FAN into your Python pipeline using PyTorch
Outcome: You have functioning landmark detection in your prototype without any licensing cost.
Use the provided code to understand landmark localization and residual blocks
Outcome: Hands-on experience with state-of-the-art architecture and a large dataset.
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 2026-09-08
Limitations
- Research code and models require PyTorch or Torch7 knowledge.
- The LS3D-W dataset is freely available after submitting an email address.
- Access is via the research paper and website, with no indication of an API or prebuilt executables.
as of 2026-08-25
Verification history
We have re-verified 2D And 3D Face Alignment 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published 2D And 3D Face Alignment tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Researchers, students, and developers who need free access to pretrained face alignment models and the LS3D-W dataset for academic or prototyping purposes.
What this tier adds
Free entry point with all four pretrained models and the full dataset available on request — no charge, but no support either.
Where the pricing makes sense
The company stage and team size where 2D And 3D Face Alignment's pricing actually pencils out — and where peers do it cheaper.
$0 for everyone — models and dataset are free to download. No API keys or usage limits. You only pay in time and effort to set it up and adapt it. Compared to commercial APIs like Microsoft Face API or AWS Rekognition, this is unbeatable for research and prototyping.
Setup time & first value
How long it actually takes to get something useful out of 2D And 3D Face Alignment — broken out by persona, not the marketing-page minute.
For a researcher familiar with PyTorch: under an hour to download and run the model. For a developer integrating into a new pipeline: a few hours. Expect a learning curve if you're new to PyTorch or Torch7.
Switching to or from 2D And 3D Face Alignment
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- ↗To a commercial API like Microsoft Face API or AWS Rekognition: if you need production support, scale, and uptime, migrate your landmark calls to a cloud service.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “2D And 3D Face Alignment”, and we withheld 6: 6 did not mention 2D And 3D Face Alignment. We are showing none, because we could not prove any of them are about 2D And 3D Face Alignment.
Official links
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Featured Head-to-Head Comparisons
2d And 3d Face Alignment vs Praktika
Praktika is the clear choice for anyone seeking to improve conversational language skills with AI-powered tutors, offering structured feedback and adaptive learning. 2D And 3D Face Alignment is a niche research tool for computer vision experts working on facial landmark detection, not a consumer product. Choose based on your goal: language fluency vs. facial analysis.
2d And 3d Face Alignment vs Surge Ai
If you're training frontier AI models and need expert human feedback for RLHF, red teaming, or custom benchmarks, Surge AI is the clear choice—its curated workforce and proprietary benchmarks (like Riemann-bench where frontier models score <10%) provide unmatched rigor. For facial landmark detection research, the free, open-source 2D And 3D Face Alignment tool offers pretrained models and the LS3D-W dataset, ideal for academic study but lacks production support. Choose based on your domain: AI alignment vs. computer vision.
Alternatives to 2D And 3D Face Alignment
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SAM 3 & 3D
Segment anything in images and video, and reconstruct 3D scenes from single images—free in Meta's playground.
SAM3DBody Cpp
Free C++ library for real-time, markerless full-body mocap from a single RGB camera.
MagicDrive
Open-source street-view generation with precise 3D geometry control for AV research
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