Stable Diffusion: From Theory to Practice

Stable Diffusion changed everything in generative AI. In this article, I explain how I applied it to create sign language translation avatars at Silence Speaks UK.

The Core Idea

Diffusion models work by gradually adding noise to data and then learning to reverse this process. This allows the model to generate new data by starting from pure noise and iteratively denoising.

Application: Sign Language Avatars

The challenge was adapting 2D video input into expressive 3D avatar animations. We used ControlNet with pose conditioning to maintain hand and body positions while generating realistic avatar appearances.

Results

The combination of MediaPipe for pose extraction and Stable Diffusion for avatar generation produced significantly more expressive and natural-looking translations compared to traditional approaches.