AI System for Sign Language Generation
Sign language interpretation in real time — critical accessibility infrastructure that most products lack. The system translates text or speech into sign language animation, providing deaf and hard of hearing users with full access to content.
System Architecture
The task breaks down into three related subtasks: translating text into sign language glosses, synthesizing gesture animation, rendering avatar.
Text-to-Gloss Translation: Sign languages are independent linguistic systems with grammar different from spoken languages. You cannot simply transliterate a word into a gesture. We use seq2seq models (MarianMT, mBART with fine-tuning) on parallel text-gloss corpora. For Russian Sign Language (RSL) and Ukrainian Sign Language available corpora are limited — partnership with deaf educators required for annotation.
Pose Estimation & Motion Synthesis:
- MediaPipe Holistic for capturing 3D poses from video references
- Motion Graph / Motion Diffusion for synthesizing smooth transitions between gestures
- Timing model for natural rhythm (pause, speed, accent)
Avatar Rendering:
- 3D avatar (Blender/Three.js) or 2D video synthesis via First Order Motion Model
- Facial expression synchronization (non-manual markers) — important part of sign grammar
- Real-time rendering via WebGL (for web platforms) or native renderer
Development Pipeline
Weeks 1–4: Define target sign language. Collect and annotate corpus with certified translators. Minimum required volume — 5–10K gesture-gloss pairs.
Weeks 5–9: Train Text-to-Gloss model. Motion capture 300–500 gestures from native signers. Build motion library.
Weeks 10–14: Develop animation synthesizer. Platform integration (web, mobile app, TV signal). Avatar development.
Weeks 15–16: Validation with deaf community participation. Iterative corrections for animation naturalness.
Supported Sign Languages
Architecture is language-independent; quality depends on training data availability. Best results for: ASL (American), BSL (British), DGS (German). For RSL — development requires corpus creation from scratch.
Technical Specifications
| Parameter | Value |
|---|---|
| Latency (text → animation start) | <500 ms (real-time mode) |
| Generation Speed | 1.5–2x real-time |
| Facial Expression Support (non-manual markers) | Yes |
| Platforms | Web (WebGL), iOS, Android, Desktop |
| Avatar Resolution | SD (720p) to HD (1080p) |
Applications
Television broadcasting (auto-captions → sign translation), educational platforms, government services (mandatory accessibility), mobile applications, interactive kiosks.
Limitations
Naturalness of machine sign language lags behind live interpreters — especially regarding idioms, humor and emotional nuances. System is optimal for informational and procedural content. For critically important communications we recommend hybrid mode with ability to switch to live interpreter.