memoavatar/memo

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1070 stars · 105 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MEMO is an open-weight diffusion model for generating expressive, identity-consistent talking videos from a single reference image and an audio track. It uses memory-guided temporal modeling and emotion-aware audio attention, with inference and finetuning scripts provided in Python.

Use cases

• generate a talking video from a photo and audio clip
• animate a portrait to match speech audio
• create expressive talking avatars with lip sync
• make a digital human sing along to a song
• finetune a talking-head model on my own video dataset
• research audio-driven video diffusion models

When to choose

• you need state-of-the-art expressive talking video generation from a single image
• you want an open-weight model you can finetune on your own data
• you have a modern NVIDIA GPU (H100 or RTX 4090) for inference

When to avoid

• you need real-time generation on modest hardware
• you only need simple lip-sync on existing video rather than generating video from a still image
• you lack a CUDA GPU

Facets

library · maturity active

video-processing deep-learning machine-learning speech-recognition llm-inference deep-learning computer-vision artificial-intelligence python cli talking-head-generation diffusion-models audio-driven-animation portrait-animation lip-sync digital-avatar research-model video audio gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="memoavatar/memo")