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MakeItTalk

MakeItTalk is open-source software that creates expressive talking-head videos from one facial image and an audio input.

Place
#20 of 28
Score
5.7
MakeItTalk's website

What MakeItTalk is

MakeItTalk is open-source software that creates expressive talking-head videos from one facial image and an audio input. Audio content guides lip movement and nearby facial motion, while speaker information influences expressions and other head dynamics. The project describes use with portraits, paintings, sketches, 2D cartoons, manga, and stylized caricatures, including faces and characters not encountered during training. Its repository includes pretrained models, training code for modules, and Google Colab demos for natural faces. For human faces and paintings, the README calls for a centered 256×256 JPG portrait and a WAV audio file. Optional command-line settings adjust lip motion on the x and y axes and head displacement. Local use requires Python 3.6, ffmpeg, and Python packages; cartoon face warping on Ubuntu also requires winehq-stable. Creating custom puppets remains on the project to-do list, and training sections for voice conversion, the speaker-aware branch, and image-to-image translation are marked as unfinished. The software is free under a CC BY-NC 4.0 license, permitting noncommercial use subject to its terms.

Good

  • Works with portraits, paintings, sketches, and stylized characters.
  • Includes pretrained models and module training code.
  • Provides Google Colab demos for natural faces.
  • Optional settings adjust lip and head motion.
  • Free under a noncommercial license.

Watch out

  • Local use requires Python 3.6 and ffmpeg.
  • Cartoon face warping on Ubuntu requires winehq-stable.
  • Custom puppets remain on the to-do list.
  • Several training sections are unfinished.

Who it suits

MakeItTalk may suit people who want to generate talking-head videos from facial images and audio, and are comfortable with its listed local requirements. Its license permits noncommercial use subject to its terms.

Verdict

MakeItTalk provides research code for generating expressive talking-head videos from an image and audio. Its noncommercial license and local setup requirements are important considerations for potential users.

Plans and prices

From MakeItTalk's own pricing page, checked 4 Oct 2026.

All about pricing
PlanPriceWhat it gives you
Open-source research codeFreeCC BY-NC 4.0 · noncommercial use only

The facts

Each one read from MakeItTalk's own pages, with the date it was checked.

What it does

Available code
The repository includes pretrained models, training code for modules, and Google Colab demos for natural faces.checked 4 Oct 2026
Custom puppets
The README lists creating custom puppets as a to-do item.checked 4 Oct 2026
Image and audio input
For human faces and paintings, the README instructs users to provide a centered 256×256 JPG portrait and WAV audio file.checked 4 Oct 2026
License
The repository license is Creative Commons Attribution-NonCommercial 4.0 International, which permits licensed use for noncommercial purposes subject to its terms.checked 4 Oct 2026
Local requirements
The README specifies Python 3.6, ffmpeg, and Python packages; cartoon face warping on Ubuntu also requires winehq-stable.checked 4 Oct 2026
Motion controls
Optional command-line arguments adjust lip motion along the x and y axes and head-motion displacement.checked 4 Oct 2026
Purpose
MakeItTalk generates expressive talking-head videos from a single facial image, using audio as the only input.checked 4 Oct 2026
Security and privacy
The repository page does not state security certifications or privacy practices.checked 4 Oct 2026
Speaker-aware animation
The method separates audio content, which controls lip and nearby facial motion, from speaker information, which influences expressions and other talking-head dynamics.checked 4 Oct 2026
Supported imagery
The project describes use with human portraits, paintings, sketches, 2D cartoons, manga, and stylized caricatures.checked 4 Oct 2026
Training status
Voice conversion, speaker-aware branch, and image-to-image translation training sections are marked “Todo...”.checked 4 Oct 2026
Unseen inputs
The method is described as generalizing to faces and characters not observed during training.checked 4 Oct 2026

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