HeyGen

HeyGen’s core engine is a multimodal transformer stack that ingests text, still images, and audio, then outputs synchronized 1080p60 video. The pipeline is modular: Scene Planning Module A fine-tuned large language model (LLM) parses the input script, identifies narrative beats, and auto-generates a shot list. The model is trained on 2.3 million high-performing marketing and training videos, enabling it to predict pacing, camera angles, and on-screen text placement that historically maximize watch time. Avatar Rendering Engine HeyGen’s photorealistic avatars are driven by a diffusion-based neural renderer that starts with a single 2D reference photo. Gaussian splatting and neural radiance fields (NeRF) are combined to extrapolate 3D facial geometry. Real-time blend-shape correction ensures lip-sync accuracy within 16 ms—below the perceptual threshold for desynchronization. Voice Cloning & Multilingual Synthesis Voice synthesis relies on a two-stage pipeline: (1) a speaker-encoder extracts vocal identity from a 10-second sample, and (2) a non-autoregressive vocoder synthesizes speech in 40+ languages. Accent and prosody transfer are handled by a cross-lingual prosody adapter trained on 12,000 hours of multilingual corpora. Asset Composition & Post-Production Visual assets (stock footage, screen recordings, or user-supplied images) are segmented with a zero-shot segmentation model. A diffusion-based inpainting network then blends foreground avatars with dynamic backgrounds, while a color-grading LUT auto-matches brand palettes pulled from a user’s style guide.