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  1. Home
  3. Condfoodgen: A Conditional Two-stream Network For Controllable Food Image Generation.
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  3. Condfoodgen: A Conditional Two-stream Network For Controllable Food Image Generation.

Related Experiment Video

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

CondFoodGen: A Conditional Two-Stream Network for Controllable Food Image Generation.

Mengyao Zhao, Hao Xiong, Weiqing Min

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 8, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Cond-FoodGen, a novel diffusion-based network, enhances food image generation by addressing limitations in texture, shape, and color. This controllable food image generation method significantly improves image quality and diversity compared to existing approaches.

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    Published on: April 17, 2021

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Food Computing

    Background:

    • Existing food image generation methods struggle with intra-class variability and food-specific challenges, leading to limited diversity and accuracy.
    • Current approaches often lack optimizations for texture, shape, and color fidelity in food images.

    Purpose of the Study:

    • To propose Cond-FoodGen, a diffusion-based two-stream network for controllable food image generation.
    • To enhance the realism, diversity, and accuracy of generated food images.

    Main Methods:

    • Utilized a two-stream network architecture with a control stream for conditional guidance and a generation stream.
    • Introduced the Bidirectional Adaptive Gating (BAG) mechanism for optimizing stream interactions and feedback.
    • Developed the Wavelet-Guided Hierarchical Attention (WGHA) module to improve fine-grained texture and structural realism.
  • Implemented a progressive multi-stage training strategy for stable optimization.

    • Main Results:

    • Cond-FoodGen consistently generates high-quality and diverse food images across three datasets.
    • Achieved an average improvement of 11.0% over existing food image generation methods.
    • Demonstrated an average improvement of 16.2% compared to leading conditional generation approaches.

    Conclusions:

    • Cond-FoodGen effectively addresses limitations in current food image generation techniques.
    • The proposed methods (BAG and WGHA) significantly enhance image fidelity and diversity.
    • The approach offers a robust solution for controllable and realistic food image synthesis.