Phase 3: Video Generation · 50 min · Python · fal.ai API · ElevenLabs API
Talking Head & Avatar Video Generation
Portrait + TTS = lip-synced avatar video. OmniHuman 1.5 makes it production-ready at $0.14/sec.
Real-world application: Talking head / avatar generation integration (OmniHuman + TTS) is a niche but growing skill — companies building training content and spokesperson videos test for this.
What you will learn
- Integrate ByteDance OmniHuman 1.5 for realistic talking-head videos from a single portrait
- Build a pipeline: text → TTS audio → OmniHuman → lip-synced video output
- Understand use cases: training content, music video performance, spokesperson videos
- Evaluate quality considerations: lighting, portrait quality, motion naturalness
Objective
Create motion evidence that can be reviewed frame by frame, not only as a successful API response. In the paired studio lab, build a complete talking head pipeline: (1) generate or provide a portrait image, (2) generate TTS audio from text using ElevenLabs, (3) generate a lip-synced video using OmniHuman via fal.ai. Track total cost and generation time.
The Problem
A company needs to produce 100 training videos with a consistent spokesperson. Hiring a human actor for each video costs $500+ per video and takes days to schedule. They need a scalable solution: provide a portrait photo and a script, get a lip-synced video of the person speaking.
This is talking head generation — combining a portrait image with TTS audio to create a video of the person speaking with lip-synced mouth movements. ByteDance's OmniHuman 1.5 makes this production-ready.
What you'll build
A complete talking head pipeline: (1) generate or provide a portrait image, (2) generate TTS audio from text using ElevenLabs, (3) generate a lip-synced video using OmniHuman via fal.ai. Track total cost and generation time.
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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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