Phantom-video/HuMo

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1283 stars · 246 forks observed · 2026-08-28

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

HuMo is a research model and Python codebase from Tsinghua University and ByteDance for human-centric video generation using collaborative multi-modal conditioning (text, reference image, and audio). It ships 1.7B and 17B model weights that generate subject-consistent, audio-visual-synchronized talking videos.

Use cases

• generate talking-head videos from a photo and audio
• create lip-synced avatar videos from text prompts
• generate videos that keep a reference person's identity consistent
• run video generation locally on a single GPU
• train or fine-tune human-centric video generation models
• use video generation inside ComfyUI workflows

When to choose

• you need subject-consistent human video generation with audio-visual sync
• you want open weights runnable on a single 32G GPU (1.7B model)
• you need a research-grade multimodal video generation model with dataset access

When to avoid

• you need fast real-time video generation
• you want a polished end-user app rather than a research codebase
• you lack a GPU - the 17B model has heavy hardware requirements

Facets

library · maturity active

video-processing machine-learning deep-learning speech-recognition llm-inference artificial-intelligence deep-learning computer-vision python video-generation talking-head audio-visual-sync diffusion-model subject-consistency multimodal comfyui huggingface video audio gpu linux docker

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For agents

markdown · JSON · MCP: product_card(name="Phantom-video/HuMo")