Keypoint & Landmark Annotation
Structure, posture and motion — captured point by point
Keypoint annotation marks the anatomical or structural points that define how something is shaped and how it moves. It powers pose estimation, gesture recognition, facial analysis, sports analytics and ergonomic studies.
- Starting at
- $0.06 / point
- Turnaround
- 24–72 hours
- Quality
- ±2 px landmark deviation
Human-labelled
Reviewed twice before delivery
One drifting joint breaks the whole skeleton
Keypoint work is unforgiving: a wrist placed two frames out of sync, or a left/right swap on a turned body, and your pose model learns a physically impossible motion.
We enforce a fixed point order, handle occluded points with explicit visibility flags, and run left/right consistency checks across every sequence.
Send us a sample batchWhat is keypoint annotation?
Keypoints (landmarks) are individual coordinates marked on defined positions of a subject — joints on a body, points around an eye, corners on a machine part — usually connected into a skeleton graph.
Each point carries a visibility state: visible, occluded, or out of frame. Getting that state right matters as much as the coordinate itself.
Where it's used
Built for real production pipelines
Sports & fitness analytics
Full-body skeletons across video for form and performance scoring.
Facial landmark datasets
68-point and custom schemas for expression and AR models.
Healthcare & ergonomics
Posture, gait and range-of-motion measurement.
Retail & apparel fit
Body landmarks for virtual try-on and sizing engines.
Sample work
Raw frame in, training-ready labels out
Real keypoint & landmark annotation output from client pilots. Drag the handle on any example to compare the source frame with what we deliver.
BeforeFull-body pose estimation
17-point COCO skeleton placed on a sprinting figure, with occluded joints flagged rather than guessed — the difference between a usable pose model and a jittery one.
17 keypoints · visibility flags · COCO keypoints
BeforeHand tracking dataset
21-point hand landmark set with every finger joint and fingertip marked, consistent left/right ordering across the whole batch for gesture recognition training.
21 points/hand · 60k frames · JSON landmarks
Samples are representative of delivered work; client-identifying frames are replaced under NDA.
How we work
A spec, a pilot, then volume — in that order
Most labelling projects fail on ambiguity, not effort. We remove the ambiguity first, prove it on a pilot, and only then scale throughput.
Start with a free pilot- 1
Define the skeleton
Point order, naming and connection graph agreed and documented before work starts.
- 2
Calibration set
Hard poses first — occlusion, rotation, motion blur — so the rules are proven on the worst frames.
- 3
Annotate
Specialists work at zoom with the skeleton overlay always on to catch impossible geometry live.
- 4
Validate & deliver
Automated smoothness and symmetry checks, then export in your training format.
Quality control
- Left/right consistency validation
- Temporal smoothness check across frames
- Visibility-flag audit
- Second-pass specialist review
What you receive
- COCO keypoints JSON
- CSV coordinate tables
- Skeleton graph definition
- Per-point visibility flags
Tools we work in
- CVAT
- Label Studio
- DeepLabCut-compatible exports
- Custom schemas
This is for you if…
Pose-estimation and gesture-recognition teams
AR/VR products needing dense facial landmarks
Sports-tech and physio platforms working from video
Every project runs under NDA with access-controlled workstations. Data is deleted on request after delivery.
Why teams switch
You have three options. Only one of them scales.
Hire in-house
- 6–10 weeks to recruit and train
- Fixed salary cost at variable volume
- QA falls on your ML engineers
Random freelancers
- Every batch looks slightly different
- No written spec, no review pass
- Disappear mid-project
Genius Clipping Path
- Dedicated pod trained on your written spec
- Two-pass QA on every single batch
- Pay per unit — scale up or pause anytime
Simple commercials
Pick the engagement that fits your stage
Pilot
Free
Up to 50 items annotated to your spec so you can benchmark quality before spending anything.
Pay per unit
$0.06 / point
Send batches whenever you need them. No minimums, no retainers, invoiced monthly.
Dedicated pod
Monthly
A fixed team reserved for your queue with guaranteed daily throughput and a named QA lead.
Not sure which fits? Tell us the dataset size below and we'll recommend the cheapest option that hits your deadline — even if that's the free pilot only.
Talk to us
Get a keypoint & landmark annotation quote — and a free pilot with it
Send your label spec and dataset size. You'll get a per-unit price, a realistic delivery date, and 50 annotated samples so you can judge quality before committing a cent.
- A real annotation specialist replies — not a sales bot
- Free 50-item pilot annotated to your written spec
- NDA signed before we touch your data
- No minimum volume and no lock-in contract
Prefer email? Write to support@geniusclippingpath.com and attach a few sample frames.
Before you get in touch
Keypoint & Landmark Annotation FAQs
Pricing, guidelines, security, rework and export formats — the answers teams usually ask for on the first call.
Can you follow our custom point schema?+
Yes. Send the point order and skeleton graph and we annotate to it exactly — no remapping needed on your side.
How do you handle occluded joints?+
They are estimated and flagged as occluded, or left out entirely — whichever your training code expects. It is a spec decision, not an annotator decision.
Do you support video sequences?+
Yes, with temporal consistency checks so joints don't jitter between frames.
Can we run a free pilot before committing?+
Yes. Send 50–100 representative images and we label them free against your guideline draft, usually within 24 hours, so you can measure quality on your own data before any invoice.
How do you handle our annotation guidelines?+
You can send an existing spec or start from a call. We turn it into a written guideline with edge-case examples, get your sign-off, and every annotator is trained and tested against it.
What happens if labels come back wrong?+
Rework is free. Flag the batch within 7 days of delivery, we correct the affected items and update the guideline so the same error class does not repeat.
How do you keep our data secure?+
NDAs on request, access-controlled workstations, no personal devices or external storage, project-scoped access per annotator, and permanent deletion after delivery when you ask for it.
Which export formats do you deliver?+
COCO JSON, YOLO txt, Pascal VOC XML, KITTI, CSV, or masks as PNG. If you use a custom schema or a platform like Labelbox, CVAT, or V7, we deliver straight into it.
How fast can you scale up?+
A dedicated pod starts at 3 annotators and scales to 100+ within about a week, with a QA lead assigned to your project throughout so quality holds as volume grows.
How does billing work?+
Pay per unit with volume tiers, or a fixed monthly dedicated-pod rate. You get an itemised invoice per batch, no setup fee, and no minimum contract on pay-per-unit work.
Still unsure? Ask us anything — we reply within one hour on business days.
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Try keypoint & landmark annotation on your own data — free
Send 50 sample frames and your label spec. You get a fully annotated, QA-reviewed pilot back within 24 hours. No card, no commitment.