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Related Experiment Video

Updated: Jun 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Scalable multimodal approach for face generation and super-resolution using a conditional diffusion model.

Ahmed Abotaleb1, Mohamed W Fakhr2, Mohamed Zaki3

  • 1Computer Engineering Department, Arab Academy for Science Technology and Maritime Transport, Cairo, 2033, Egypt. eng.ahmed.gamal.411@gmail.com.

Scientific Reports
|November 8, 2024
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Summary

This study introduces Speaking the Language of Faces (SLF), a multimodal system for face image generation and super-resolution using diffusion models. Speaker embeddings are sufficient audio features, with audio signals profoundly influencing results more than low-resolution images.

Keywords:
Diffusion probabilistic modelsScalable multimodal approachSpeaker embeddingsSpeech conditioned face generationSpeech conditioned face super-resolution

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multimodal conditioned face image generation and super-resolution are key research areas.
  • Diffusion models are increasingly utilized for high-fidelity image synthesis tasks.

Purpose of the Study:

  • To introduce a flexible, modular, and simple multimodal system called Speaking the Language of Faces (SLF).
  • To present a scalability scheme and sensitivity analysis for practitioners in system parameter estimation and feature selection.

Main Methods:

  • Developed SLF, a system comprising an encoder (feature vector generator) and a decoder (image generator) using a conditional diffusion model.
  • SLF accepts diverse inputs: low-resolution images, speech signals, and person attributes (age, gender, ethnicity).
  • Implemented a scalability scheme based on conditional scale values and conducted sensitivity analysis across multiple system versions.

Main Results:

  • SLF demonstrated versatility in tasks like speech-to-face generation and conditioned face super-resolution.
  • Speaker embeddings were identified as sufficient audio features.
  • Audio signals had a profound impact, exceeding that of low-resolution images (8x8), though image resolution remained significant.
  • Demographic attributes (gender, ethnicity, age) showed moderate influence.

Conclusions:

  • The Speaking the Language of Faces (SLF) system offers a versatile approach to multimodal face image generation and super-resolution.
  • Conditional scale values significantly influence system behavior and performance, highlighting their importance in parameter tuning.
  • Sensitivity analysis confirmed the substantial impact of audio features and the moderate effect of demographic attributes on generated face images.