28 Days of SignLLM Pipelines
Learn how sign-language AI data moves from real signing videos into machine-readable training data. This course explains capture, pose/keypoint extraction, ASL Gloss, NPZ datasets, training, evaluation, and human quality review in plain English.
Course Overview
SignLLMs are AI systems designed to work with sign-language data. Some systems try to recognize signs from video. Others try to produce sign-language motion from text, gloss, or prompts. This course focuses on the development pipeline behind these systems.
A SignLLM is not built by simply uploading videos and pressing train. The hard work is in the middle: collecting usable videos, extracting body, hand, and face motion, converting that motion into consistent files, labeling the meaning, checking quality, correcting mistakes, and testing whether the model learned anything useful.
For ASL, meaning is carried through handshape, movement, palm orientation, location, facial expression, body posture, timing, and context. A missing fingertip, wrong wrist angle, bad crop, or weak facial signal can change the meaning.
Foundations
What SignLLMs are and why sign-language AI is different.
What a SignLLM Is
GoalUnderstand SignLLMs as AI systems for sign-language recognition, translation, or production.
OpenStart Day 1 lesson.
Why Sign Language AI Is Different
GoalUnderstand why sign-language AI is harder than text-only AI.
OpenStart Day 2 lesson.
Recognition vs Production
GoalSeparate sign recognition from sign production.
OpenStart Day 3 lesson.
The Basic Pipeline
GoalLearn the full pipeline at a high level.
OpenStart Day 4 lesson.
What the Model Learns From
GoalUnderstand that models learn from processed examples, not human meaning directly.
OpenStart Day 5 lesson.
Why Pose Data Is Used
GoalUnderstand why researchers convert videos into pose/keypoints.
OpenStart Day 6 lesson.
Week 1 Review
GoalBuild a clear beginner explanation of SignLLM development.
OpenStart Day 7 lesson.
Data Pipeline
Video capture, pose extraction, ASL Gloss, NPZ, and metadata.
Capturing Signer Video
GoalLearn what makes source video useful.
OpenStart Day 8 lesson.
Frames and Timing
GoalUnderstand why video becomes frame sequences.
OpenStart Day 9 lesson.
Pose and Keypoint Extraction
GoalUnderstand pose/keypoint extraction in plain English.
OpenStart Day 10 lesson.
Body, Hands, and Face
GoalLearn why whole-body tracking matters for ASL.
OpenStart Day 11 lesson.
ASL Gloss
GoalUnderstand ASL Gloss as a bridge label.
OpenStart Day 12 lesson.
NPZ Files
GoalUnderstand why pose data may be saved as NPZ.
OpenStart Day 13 lesson.
Week 2 Review
GoalConnect video, gloss, keypoints, and NPZ.
OpenStart Day 14 lesson.
Training and Review
Datasets, splits, training, validation, inspection, and correction.
Dataset Curation
GoalUnderstand dataset curation as careful selection and organization.
OpenStart Day 15 lesson.
Annotation and Labeling
GoalLearn why labels are expensive and important.
OpenStart Day 16 lesson.
Manual Visual Inspection
GoalUnderstand why humans must inspect pose outputs.
OpenStart Day 17 lesson.
Editing and Correcting Signs
GoalLearn what correction can involve.
OpenStart Day 18 lesson.
Training
GoalUnderstand training in plain English.
OpenStart Day 19 lesson.
Validation
GoalUnderstand validation as testing during development.
OpenStart Day 20 lesson.
Week 3 Review
GoalSummarize why SignLLM work takes many hours.
OpenStart Day 21 lesson.
Responsible Use and Final Pipeline
Evaluation, limitations, privacy, ethics, and a final pipeline plan.
Evaluation Metrics
GoalLearn that evaluation depends on the task.
OpenStart Day 22 lesson.
Common Failure Modes
GoalRecognize typical failures.
OpenStart Day 23 lesson.
Privacy and Consent
GoalUnderstand privacy risks in sign-language datasets.
OpenStart Day 24 lesson.
Deaf-First Quality Control
GoalCenter Deaf language knowledge in the process.
OpenStart Day 25 lesson.
Limitations and Honest Claims
GoalLearn how to avoid overclaiming.
OpenStart Day 26 lesson.
Building a Small Prototype Pipeline
GoalDesign a realistic starter project.
OpenStart Day 27 lesson.
Final Project: SignLLM Pipeline Map
GoalCreate a complete learning artifact.
OpenStart Day 28 lesson.
Glossary
- SignLLM
- A sign-language AI model or system that works with sign-language data. It may recognize signs, translate signs, generate gloss, generate pose, or help produce avatar signing.
- ASL Gloss
- A written label system used to represent ASL signs. It helps connect sign video to text labels, but it is not the same as full ASL or full English.
- Pose / Keypoints
- Numeric points that mark body parts such as shoulders, elbows, wrists, fingertips, eyes, mouth, and head position.
- NPZ
- A compressed NumPy file used to store arrays. In sign-language pipelines, NPZ files may hold pose features, masks, frame sequences, and related data.
- Dataset
- A structured collection of examples used to train or test a model. For SignLLMs, this may include videos, gloss labels, pose files, metadata, and quality notes.
- Annotation
- Human or assisted labeling of data. This can include gloss, translation, frame boundaries, sign labels, quality flags, signer details, and corrections.