Sign language translation needs multimodal understanding, not just pose estimation
Q1Most sign language AI systems treat the problem as pose estimation + classification. Track hand positions, match to a dictionary of signs. But sign languages are far more complex — they use simultaneous channels (both hands, facial expressions, body posture, spatial reference), and the meaning changes with context, speed, and spatial relationships. We're building our system on multimodal transformers that process all channels simultaneously.
Q2Data is our biggest bottleneck. We're working with Deaf community partners to build annotation tools that are accessible and respect the cultural context of Deaf communication. It's slower than scraping videos, but the quality difference is enormous.