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GANimate: Ultra-Efficient Lip-Landmark-Driven Talking Face Animation Using a Learned Kalman Filter on GAN Feature

Ethan Fenakel¹, Ben Ohayon¹, Dan Raviv¹

¹School of Electrical and Computer Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

GANimate offers a lightweight method for animating talking faces using Generative Adversarial Networks (GANs). This efficient approach works on low-resource devices, creating realistic lip movements from 2D landmarks.

Keywords:

GANs animation facial animation interactive image manipulation video generation

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Existing talking face synthesis methods often require significant computational resources, limiting their use on edge devices.
Approaches using diffusion models, transformers, or 3DMMs are computationally intensive and have high memory demands.
There is a need for efficient and accessible methods for real-time talking face animation on mobile and low-resource platforms.

Purpose of the Study:

To introduce GANimate, a novel, lightweight method for animating talking faces.
To enable efficient and high-quality talking face synthesis on low-memory, low-compute edge devices.
To provide a method easily integrable with existing lip-landmark generators without pre-training.

Main Methods:

Leverages latent-space manipulation of Generative Adversarial Networks (GANs).
Utilizes 2D lip landmarks extracted from standard mobile vision-sensor inputs as geometric constraints.
Employs a Kalman filter for stable tracking and adaptive refinement of lip landmarks during video synthesis.

Main Results:

Achieves realistic and expressive lip movements by optimizing in the GAN feature latent space.
Produces stable and visually coherent animations with minimal computational overhead.
Demonstrates efficient operation on low-resource edge devices, bridging performance and accessibility.

Conclusions:

GANimate offers a practical solution for real-time talking face synthesis on edge devices.
The method's lightweight design and no pre-training requirement enhance its accessibility and integration.
Represents a significant advancement towards lifelike, real-time avatars for mobile human-computer interaction.