2D And 3D Face Alignment is live · free · last verified 22 Oct 2026 · viability monitor
2D And 3D Face Alignment
2D and 3D face alignment research code — 68-point 2D-FAN and 3D-FAN landmark models plus the 230,000-image LS3D-W dataset.
The 2D-to-3D-FAN converter is the piece most teams undervalue: if your 2D annotations follow a consistent convention, you can generate 3D labels at scale without new collection, and LS3D-W's full release removed the old excuse for not benchmarking 3D alignment. The PyTorch library is the closest thing to tooling here — TWO_D and THREE_D modes with automatic weight selection on install. It is research code, so treat it as a baseline to extend, not a dependency for a production face product.
Verdict generatedVerified 8d ago · liveness 55/100 · cite: rightaichoice.com/tools/2d-and-3d-face-alignment
- Computer vision researchers who need a reproducible 2D or 3D face alignment baseline
- Teams converting consistently annotated 2D landmarks into 3D labels at scale with 2D-to-3D-FAN
- Groups benchmarking 3D facial landmark models against LS3D-W
- Developers prototyping facial analysis with pretrained PyTorch checkpoints
- Teams that need a hosted landmark API with an uptime commitment
- Products that require a support contract or a maintained application around the models
- Real-time mobile deployment without further optimization and export work
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Skip 2D And 3D Face Alignment if you need a hosted landmark endpoint or a maintained pipeline with commercial support — this is ICCV 2017 research code you integrate and extend yourself.
The 3D path costs you a second checkpoint download and more compute than the 2D path, because THREE_D combines 3D-FAN with a separate depth network (224 MiB of depth weights on top of 91 MiB of 3D-FAN in the PyTorch 1.5
The entire release — PyTorch library, both framework implementations and the LS3D-W dataset — is published as open research output at no charge. That puts it below any commercial face-analysis vendor on cost, provided you can absorb the integration work yourself. Teams that price out a hosted landmark API are really buying maintenance and uptime; this project does not sell either.
In short
2D And 3D Face Alignment — 2D and 3D face alignment research code — 68-point 2D-FAN and 3D-FAN landmark models plus the 230,000-image LS3D-W dataset. Best for Computer vision researchers who need a reproducible 2D or 3D face alignment baseline, Teams converting consistently annotated 2D landmarks into 3D labels at scale with 2D-to-3D-FAN, Groups benchmarking 3D facial landmark models against LS3D-W. Free to use.
What is 2D And 3D Face Alignment?
2D And 3D Face Alignment — 2D and 3D face alignment research code — 68-point 2D-FAN and 3D-FAN landmark models plus the 230,000-image LS3D-W dataset. Best for Computer vision researchers who need a reproducible 2D or 3D face alignment baseline, Teams converting consistently annotated 2D landmarks into 3D labels at scale with 2D-to-3D-FAN, Groups benchmarking 3D facial landmark models against LS3D-W. Free to use.
2D And 3D Face Alignment is the project page for an ICCV 2017 paper on how close a very deep network gets to saturating 2D and 3D face alignment benchmarks. You get code and data rather than a product: four pretrained landmark models, two framework implementations, and LS3D-W, roughly 230,000 faces annotated in a consistent 68-point 3D scheme. As of July 2024 the LS3D-W release is fully available to request through the email form on the project page. The fast route is the PyTorch library — pip install face-alignment pulls the package and downloads weights matched to your installed version automatically. You instantiate FaceAlignment with LandmarksType.TWO_D for 68 image-plane (x, y) points or THREE_D for x, y plus estimated depth z; 3D output combines the 3D-FAN network with a separate depth network, so the THREE_D path pulls a larger checkpoint. For reproducing the paper's numbers, the original Torch7 release stays available with 2D-FAN, 3D-FAN, 2D-to-3D-FAN and 3D-FAN-depth weights. The 2D-to-3D-FAN model is the sleeper for data work: given consistently annotated 2D landmarks and the source image, it converts them into the projected 3D convention — exactly how LS3D-W was assembled out of AFLW, 300VW, 300W and FDDB. Dataset and weights are requested through an email form that reveals the download links. If you need a reproducible alignment baseline or a large 3D landmark benchmark, this is the reference implementation. If you need a hosted landmark API or a drop-in on-device pipeline, look at commercial face-analysis vendors.
Key features
- 2D facial landmark detection with 68 points via 2D-FAN
- 3D facial landmark detection with x, y and depth z via 3D-FAN
- Separate depth network estimating landmark depth for the 3D path
- 2D-to-3D-FAN converts compatible 2D annotations into the projected 3D convention
- PyTorch library installed with pip install face-alignment
- Automatic selection and download of weights matching your library version
- TWO_D and THREE_D landmark modes via LandmarksType in the Python API
- get_landmarks_from_image() for single-image inference
- CPU device support in the Python example (device="cpu")
- PyTorch v1.5 checkpoints: 91 MiB for 2D-FAN, 91 MiB plus 224 MiB depth model for 3D-FAN
- Original Torch7 implementations: 2D-FAN, 3D-FAN, 2D-to-3D-FAN and 3D-FAN-depth
- LS3D-W dataset of roughly 230,000 images with consistent 68-point 3D landmarks
- LS3D-W balanced subset alongside the full release
- Dataset access via an email request form that reveals download links
- Unified annotations drawn from AFLW, 300VW, 300W and FDDB
Behind the verdict
Pick this when you need a published, reproducible baseline for 68-point 2D or 3D facial landmark detection and want weights you can actually download. The PyTorch package is genuinely quick to stand up — FaceAlignment with LandmarksType.TWO_D or THREE_D, get_landmarks_from_image() for single images, CPU support in the example. The 3D path costs more: you pull a separate depth model (about 224 MiB alongside the 91 MiB 2D checkpoint) and merge its output with the 3D-FAN points. In practice, the data pipeline is the reason to be here. If your 2D landmark annotations follow a consistent 68-point convention, 2D-to-3D-FAN will project them into the same 3D scheme LS3D-W uses, guided by the source image. That is how you build a 3D landmark set without starting a fresh collection, and it is how LS3D-W itself was assembled from AFLW, 300VW, 300W and FDDB. Pass when you need a hosted endpoint with an uptime commitment, a support contract, or an on-device mobile pipeline you can ship without export and optimization work. This is ICCV 2017 research; there is no product team behind it, no release cadence to plan around, and no SLA. If your product roadmap depends on one of those, compare commercial face-analysis vendors instead — they will cost money but they come with a maintenance story. Where it bites: the release is a baseline, not a tuned library. You will be reading the code and the paper to understand what the depth network is doing, and you will be doing your own packaging if you want it inside an application. The Torch7 implementations are there for reproducing the paper's numbers, not for new work. One thing that changed recently: the complete LS3D-W dataset of roughly 230,000 3D facial landmark images is now available to request through the email form on the project
Models under the hood
as of 2026-10-24
Limitations
- This is research code from an ICCV 2017 paper, not a finished product.
- You work from the paper, the project page and the repository, which means PyTorch or Torch7 fluency.
- The 3D path is heavier than the 2D path: it combines 3D-FAN with a separate depth network, so the THREE_D mode downloads a second checkpoint (a 224 MiB depth file alongside the 91 MiB 3D-FAN weights in the PyTorch 1.5 release).
- LS3D-W is not a direct download — you enter an email address to display the full-dataset and balanced-subset links.
- The annotations and models follow a 68-point convention, so downstream stacks using a different landmark topology need their own conversion.
- The paper's own finding is that performance is close to saturating the datasets evaluated in 2017.
as of 2026-10-12
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Is 2D And 3D Face Alignment still active?
2D And 3D Face Alignment is live · free · last verified 22 Oct 2026 · viability monitor.
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: October 2026
How we score →What do users say about 2D And 3D Face Alignment?
What people actually say about 2D And 3D Face Alignment — is it worth it?
Our community sentiment research written Jul 6, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about 2D And 3D Face Alignment— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 22 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified 2D And 3D Face Alignment 11 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.
- — changed SSL certificate
- — changed pricing tiers, description, our verdict, summary, features, who it suits, who should skip it
- — changed pricing fit, description, our verdict, switching out, pricing tiers, use cases, features, switching in, who it suits, hidden costs, who should skip it, limitations, summary, setup time
- — changed limitations, pricing page
- — changed summary, who it suits, who should skip it, our verdict, pricing tiers, description, features
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How much does 2D And 3D Face Alignment cost in 2026?
2D And 3D Face Alignment: Free to use. Pricing last verified 22 Oct 2026.
Plans compared
Every published 2D And 3D Face Alignment tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free (research release) | Free | — | Computer vision researchers, academic groups and developers who can integrate PyTorch or Torch7 code themselves and want a reproducible face alignment baseline.Adds: Starting tier and the only tier: 2D-FAN and 3D-FAN checkpoints, the depth network, 2D-to-3D-FAN, both framework implementations and the LS3D-W dataset, all at no charge. |
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.
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.
The entire release — PyTorch library, both framework implementations and the LS3D-W dataset — is published as open research output at no charge. That puts it below any commercial face-analysis vendor on cost, provided you can absorb the integration work yourself. Teams that price out a hosted landmark API are really buying maintenance and uptime; this project does not sell either.
Has 2D And 3D Face Alignment's pricing changed?
No plan price for 2D And 3D Face Alignment has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick 2D And 3D Face Alignment?
Pick 2D And 3D Face Alignment if you are Computer vision researchers who need a reproducible 2D or 3D face alignment baseline, Teams converting consistently annotated 2D landmarks into 3D labels at scale with 2D-to-3D-FAN, Groups benchmarking 3D facial landmark models against LS3D-W, Developers prototyping facial analysis with pretrained PyTorch checkpoints.
Look elsewhere if you are Teams that need a hosted landmark API with an uptime commitment, Products that require a support contract or a maintained application around the models, Real-time mobile deployment without further optimization and export work, Projects expecting new model releases on an active cadence — the paper dates to 2017.
Skip it if Skip 2D And 3D Face Alignment if you need a hosted landmark endpoint or a maintained pipeline with commercial support — this is ICCV 2017 research code you integrate and extend yourself.
GeneratedReal-world workflow fit
Concrete scenarios for the personas 2D And 3D Face Alignment actually fits — and what changes day-one when you adopt it.
You need a strong 2D baseline before publishing a new alignment method, so you install the library, run LandmarksType.TWO_D over your test images with get_landmarks_from_image(), and compare against the paper's reported numbers.
Outcome: A reproducible 2D-FAN baseline in place, with the ICCV 2017 results and project page as the reference point for your write-up.
Your existing corpus has consistently annotated 2D landmarks but no depth, so you run 2D-to-3D-FAN over it, guided by the source images, to bring it into the projected 3D convention.
Outcome: 3D labels at scale without a new collection campaign, aligned to the same convention LS3D-W uses.
You pip install face-alignment, switch the LandmarksType to THREE_D for x, y and z output, and feed the landmarks into a pose-sensitive downstream pipeline.
Outcome: A working landmark step in your prototype, with the caveat that shipping to mobile or an uptime-bound product needs optimization and export work you own.
Use cases
- Run 68-point 2D landmark detection on in-the-wild images with the 2D-FAN model
- Predict 68-point 3D landmarks with 3D-FAN for pose-sensitive facial analysis
- Convert existing, consistently annotated 2D points into the 3D convention with 2D-to-3D-FAN
- Train or fine-tune the provided models on your own dataset
- Benchmark a new face alignment algorithm against LS3D-W
- Reproduce the ICCV 2017 paper's numerical evaluations using the Torch7 checkpoints
- Feed landmark output into a downstream face analysis pipeline via get_landmarks_from_image()
- Study the effect of pose, resolution, initialization and network size on landmark accuracy
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.
Researchers already fluent in PyTorch reach a first landmark prediction in minutes: pip install face-alignment, instantiate FaceAlignment with LandmarksType.TWO_D, and call get_landmarks_from_image() — the library fetches matching weights on install. The THREE_D path adds the depth-network checkpoint download. Getting LS3D-W itself is the longer step, since access runs through the email request
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.
- →From a manual or in-house landmark pipeline: swap the detector for FaceAlignment with LandmarksType.TWO_D to get 68-point (x, y) output from get_landmarks_from_image().
- →From 2D-only annotations: run 2D-to-3D-FAN over consistently annotated landmarks, guided by the source image, to obtain the projected 3D convention.
- →From a commercial landmark API: re-point your inference call at the local PyTorch library, accepting that you take on deployment, scaling and maintenance yourself.
- ↗To a commercial face-analysis vendor: replace the local library call with a hosted landmark endpoint when you need uptime commitments, support or on-device packaging.
- ↗To a different 2D landmark model: re-run inference and remap the 68-point output if your new model uses a different landmark topology.
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
What are the best 2D And 3D Face Alignment alternatives?
The closest alternatives to 2D And 3D Face Alignment we list are 3dmatch Toolbox, YuzuMarker.FontDetection and SAM 3 & 3D. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| 3dmatch Toolbox | Free | Free | — |
| YuzuMarker.FontDetection | Free | Free | — |
| SAM 3 & 3D | Free | Free | — |
Alternatives to 2D And 3D Face Alignment
View all
3dmatch Toolbox
3DMatch is a Princeton research codebase and benchmark suite for learning local 3D geometric descriptors from RGB-D reconstructions.
YuzuMarker.FontDetection
Free browser tool that identifies Chinese, Japanese, and Korean typefaces from an uploaded image and returns confidence scores plus weight, slant, and stroke
SAM 3 & 3D
SAM 3 does text-prompt, open-vocabulary segmentation and tracking in images and video — free to try in Meta's playground.
Tools that pair well with 2D And 3D Face Alignment
Common stack mates teams adopt alongside 2D And 3D Face Alignment, with the specific reason each pairing earns its keep.
3dmatch Toolbox
3DMatch is a Princeton research codebase and benchmark suite for learning local 3D geometric descriptors from RGB-D reconstructions.
YuzuMarker.FontDetection
Free browser tool that identifies Chinese, Japanese, and Korean typefaces from an uploaded image and returns confidence scores plus weight, slant, and stroke
SAM 3 & 3D
SAM 3 does text-prompt, open-vocabulary segmentation and tracking in images and video — free to try in Meta's playground.
2D And 3D Face Alignment vs its closest rival
2D And 3D Face Alignment's closest head-to-head is with 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. Verdict generated
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.
Generated2d 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.
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