Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Structure of Lipids

Structure of Lipids

What are Lipids?

What are Lipids?

Lipid Digestion

Lipid Digestion

Lipids are large molecules that are generally not water-soluble. Since most of the digestive enzymes in the human body are water-based, there are specific steps the body must take to break down lipids and make them available for use.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Published on: June 14, 2014

FlexLip: A Controllable Text-to-Lip System.

Dan Oneață¹, Beáta Lőrincz², Adriana Stan³

¹Speech and Dialogue Research Lab, University "Politehnica" of Bucharest, 060042 Bucharest, Romania.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

FlexLip converts text to lip landmarks using a modular system. It achieves comparable results with minimal training data, enabling efficient, controllable synthetic media generation.

Keywords:

artificial intelligence deep learning generative models objective measures speech synthesis speech-to-lip text-to-lip text-to-speech zero-shot adaptation

More Related Videos

µTongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo

µTongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo

Published on: April 22, 2021

Antibody Profiling by Luciferase Immunoprecipitation Systems LIPS

Antibody Profiling by Luciferase Immunoprecipitation Systems LIPS

Published on: October 7, 2009

Related Experiment Videos

Last Updated: Sep 20, 2025

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Stimulating the Lip Motor Cortex with Transcranial Magnetic Stimulation

Published on: June 14, 2014

µTongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo

µTongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo

Published on: April 22, 2021

Antibody Profiling by Luciferase Immunoprecipitation Systems LIPS

Antibody Profiling by Luciferase Immunoprecipitation Systems LIPS

Published on: October 7, 2009

Area of Science:

Artificial Intelligence
Computer Vision
Speech Processing

Background:

Text-to-video generation is a growing area in synthetic media.
Existing methods show near-natural performance in specific tasks.
Generating realistic lip movements from text is a key challenge.

Purpose of the Study:

To develop a modular and controllable system for text-to-lip landmark generation.
To evaluate individual components of the text-to-video pipeline.
To enable efficient adaptation to new speaker identities.

Main Methods:

A modular system, FlexLip, comprising text-to-speech and speech-to-lip modules.
Deep neural network architectures with controllable components.
Evaluation of system performance with minimal training data (20 min audio, 5 min lip).
Introduction of objective evaluation measures for the complete system flow.

Main Results:

Objective measures for generated lip landmarks are comparable to systems using larger datasets.
Efficient adaptation to new speaker identities is achieved with minimal data.
Zero-shot lip adaptation to unseen identities is demonstrated by updating lip shape models.

Conclusions:

FlexLip offers a modular and controllable approach to text-to-lip landmark generation.
The system demonstrates high efficiency, requiring significantly less training data.
It enables effective speaker adaptation, including zero-shot capabilities for synthetic media.