Readable layout
---
name: ai-avatar-video
description: "Create AI avatar and talking head videos via inference.sh CLI. Recommended: P-Video-Avatar (fastest, cheapest, built-in TTS). Also: OmniHuman, Fabric, PixVerse. Audio: Inworld TTS-2 (100+ languages, emotion steering for characters), ElevenLabs, Kokoro. Capabilities: audio-driven avatars, text-to-avatar, lipsync videos, talking head generation, virtual presenters, UGC content. Use for: AI presenters, explainer videos, virtual influencers, dubbing, marketing videos, UGC ads, gaming avatars, NPC dialogue. Triggers: ai avatar, talking head, lipsync, avatar video, virtual presenter, ai spokesperson, audio driven video, heygen alternative, synthesia alternative, talking avatar, lip sync, video avatar, ai presenter, digital human, ugc, ugc video, ugc ad, avatar ugc"
allowed-tools: Bash(belt *)
---
> **Install the belt CLI skill:** `npx skills add belt-sh/cli`
# AI Avatar & Talking Head Videos
Create AI avatars and talking head videos via [inference.sh](https://inference.sh) CLI.
## Quick Start
> Requires inference.sh CLI (`belt`). [Install instructions](https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md)
```bash
belt login
# Recommended: P-Video-Avatar (fastest, cheapest, built-in TTS)
belt app run pruna/p-video-avatar --input '{
"image": "https://portrait.jpg",
"voice_script": "Hello, welcome to our product demo!",
"voice": "Zephyr (Female)"
}'
```
## Available Models
**Start with P-Video-Avatar** — it's 18x faster and 6x cheaper than alternatives, with built-in TTS, dynamic backgrounds, and 1080p support.
| Model | App ID | Best For | Built-in TTS |
|-------|--------|----------|-------------|
| **P-Video-Avatar** | `pruna/p-video-avatar` | **Best overall: speed, cost, quality, control** | **Yes (30 voices, 10 languages)** |
| OmniHuman 1.5 | `bytedance/omnihuman-1-5` | Multi-character, audio-driven | No |
| Fabric 1.0 | `falai/fabric-1-0` | Image talks with lipsync | Yes |
| PixVerse Lipsync | `falai/pixverse-lipsync` | Lipsync an existing video | Yes |
### Cost & Speed Comparison
| Model | Speed (per sec of video) | Cost per second |
|-------|-------------------------|----------------|
| **P-Video-Avatar** | **~1.83s/s** | **$0.025** |
| OmniHuman 1.5 | ~28s/s (15x slower) | $0.16 (6.4x more) |
| Fabric 1.0 | ~34s/s (18x slower) | $0.14 (5.6x more) |
## Examples
### P-Video-Avatar (Recommended)
Generate avatar from portrait + text script with built-in TTS:
```bash
belt app run pruna/p-video-avatar --input '{
"image": "https://portrait.jpg",
"voice_script": "Welcome to our product walkthrough. Today I will show you three key features.",
"voice": "Puck (Male)",
"voice_language": "English (US)",
"resolution": "720p"
}'
```
With custom style control:
```bash
belt app run pruna/p-video-avatar --input '{
"image": "https://portrait.jpg",
"voice_script": "This is exciting news!",
"voice": "Aoede (Female)",
"voice_prompt": "Enthusiastic and energetic tone",
"video_prompt": "The person is presenting on stage with dramatic lighting",
"resolution": "1080p"
}'
```
With audio file instead of TTS:
```bash
belt app run pruna/p-video-avatar --input '{
"image": "https://portrait.jpg",
"audio": "https://speech.mp3"
}'
```
### Full Workflow: Generate Portrait + Avatar
Use Pruna P-Image to generate the portrait, then create the avatar:
```bash
# 1. Generate a portrait image
belt app run pruna/p-image --input '{
"prompt": "professional headshot portrait of a young woman, neutral background, looking at camera, studio lighting, photorealistic",
"aspect_ratio": "9:16"
}'
# 2. Create avatar video with built-in TTS
belt app run pruna/p-video-avatar --input '{
"image": "",
"voice_script": "Hi there! Let me walk you through our latest features.",
"voice": "Zephyr (Female)"
}'
```
### OmniHuman 1.5 (Multi-Character)
```bash
belt app run bytedance/omnihuman-1-5 --input '{
"image": "https://portrait.jpg",
"audio": "https://speech.mp3"
}'
```
Supports specifying which character to drive in multi-person images.
### Fabric 1.0 (Image Talks)
```bash
belt app run falai/fabric-1-0 --input '{
"image": "https://face.jpg",
"audio": "https://audio.mp3"
}'
```
### PixVerse Lipsync
```bash
belt app run falai/pixverse-lipsync --input '{
"video": "https://talking-head.mp4",
"audio": "https://speech.mp3"
}'
```
Takes a video, not a still image. Omit `audio` and pass `text` (plus optional `voice_id`) to use the built-in TTS.
## Full Workflow: TTS + Avatar (Non-TTS Models)
For models without built-in TTS (OmniHuman), generate speech first:
```bash
# 1. Generate speech — Inworld TTS-2 for expressive character voices
belt app run inworld/text-to-speech-2 --input '{
"text": "[friendly] Welcome to our product demo! [excited] Let me show you three features that will change how you work.",
"voice_id": "Sarah",
"delivery_mode": "CREATIVE"
}' > speech.json
# 2. Create avatar video with the speech
belt app run bytedance/omnihuman-1-5 --input '{
"image": "https://presenter-photo.jpg",
"audio": ""
}'
```
> **Tip**: For most use cases, P-Video-Avatar with built-in TTS is simpler — no separate audio step needed. Use this workflow only when you specifically need OmniHuman (multi-character) or PixVerse (realistic lipsync).
## Full Workflow: Dub Video in Another Language
```bash
# 1. Transcribe original video
belt app run infsh/fast-whisper-large-v3 --input '{"audio": "https://video.mp4"}' > transcript.json
# 2. Translate text (manually or with an LLM)
# 3. Generate speech in new language
belt app run falai/kokoro-tts --input '{"prompt": ""}' > new_speech.json
# 4. Lipsync the original video with new audio
belt app run infsh/latentsync-1-6 --input '{
"video_path": "https://original-video.mp4",
"audio_path": ""
}'
```
## Avatar UGC Generation
Create UGC-style content with P-Video-Avatar — built-in TTS, no separate audio step needed:
```bash
# 1. Generate a relatable UGC-style portrait
belt app run pruna/p-image --input '{
"prompt": "casual selfie-style photo of a young woman in a cozy room, natural lighting, looking at camera, warm smile, authentic feel",
"aspect_ratio": "9:16"
}'
# 2. Create UGC avatar video with built-in TTS
belt app run pruna/p-video-avatar --input '{
"image": "",
"voice_script": "Okay so I just tried this product and honestly? It is a game changer. I was not expecting to love it this much but here we are!",
"voice": "Zephyr (Female)",
"voice_prompt": "Excited, casual, authentic tone like talking to a friend",
"video_prompt": "The person is talking casually to camera in their room, natural gestures",
"resolution": "1080p"
}'
```
### Why P-Video-Avatar for UGC
- **All-in-one** — built-in TTS means no separate audio generation step
- **30 voices, 10 languages** — match your target audience
- **Voice + video prompts** — control tone, emotion, body language, and background independently
- **18x faster, 6x cheaper** — produce UGC at scale vs. Fabric/OmniHuman/HeyGen
- **1080p support** — platform-ready vertical video from a single portrait image
### Batch UGC: Same Product, Multiple Presenters
```bash
# Generate 3 different presenters
for voice in "Zephyr (Female)" "Puck (Male)" "Aoede (Female)"; do
belt app run pruna/p-video-avatar --input "{
\"image\": \"https://portrait.jpg\",
\"voice_script\": \"This changed my morning routine completely. Five minutes and I am done.\",
\"voice\": \"$voice\",
\"voice_prompt\": \"Casual, authentic, like a real testimonial\",
\"video_prompt\": \"Person talking to camera in a bright kitchen\",
\"resolution\": \"1080p\"
}"
done
```
## Use Cases
- **UGC & Marketing**: Product demos, UGC-style ads with AI presenters
- **Education**: Course videos, explainers
- **Localization**: Dub content across 10 languages from one image
- **Social Media**: Consistent virtual influencer content
- **Corporate**: Training videos, announcements
- **Gaming**: Character avatars, NPC dialogue
## Tips
- Use high-quality portrait photos (front-facing, good lighting)
- Audio should be clear with minimal background noise
- P-Video-Avatar supports built-in TTS — no need for a separate speech generation step
- P-Video-Avatar output aspect ratio matches the input image
- Generate portraits with `pruna/p-image` using `9:16` aspect ratio for vertical videos
- OmniHuman 1.5 supports multiple people in one image
- LatentSync is best for syncing existing videos to new audio
## Related Skills
```bash
# Dedicated P-Video-Avatar skill
npx skills add inference-sh/skills@p-video-avatar
# Full platform skill (all apps)
npx skills add inference-sh/skills@infsh-cli
# Text-to-speech (generate audio for non-TTS avatar models)
npx skills add inference-sh/skills@text-to-speech
# Speech-to-text (transcribe for dubbing)
npx skills add inference-sh/skills@speech-to-text
# Video generation
npx skills add inference-sh/skills@ai-video-generation
# Image generation (create avatar images)
npx skills add inference-sh/skills@ai-image-generation
```
Browse all video apps: `belt app list --category video`
## Documentation
- [Running Apps](https://inference.sh/docs/apps/running) - How to run apps via CLI
- [Content Pipeline Example](https://inference.sh/docs/examples/content-pipeline) - Building media workflows
- [Streaming Results](https://inference.sh/docs/api/sdk/streaming) - Real-time progress updates
