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FreqPose: Frequency-Aware Diffusion with Fractional Gabor Filters and Global Pose-Semantic Alignment.

Meng Wang1, Bing Wang1, Huiling Chen2

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 727 South Jingming Road, Kunming 650500, China.

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Summary

This study introduces a diffusion-based generative framework for pose-guided person image generation, enhancing texture details and maintaining semantic identity during pose changes. The novel approach improves image fidelity and consistency, outperforming existing methods.

Keywords:
diffusion modelsfractional gabor filtersfrequency-aware feature extractionglobal semantic-pose alignmenthigh-fidelity texture reconstructionpose-guided person image synthesis

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Generation

Background:

  • Pose-guided person image generation faces challenges in preserving high-frequency texture details and semantic identity during pose transitions.
  • Existing methods often struggle with blurriness and loss of detail in appearance transfer and consistency during pose changes.

Purpose of the Study:

  • To propose a novel diffusion-based generative framework that addresses texture detail loss and semantic inconsistency in pose-guided person image generation.
  • To enhance image detail fidelity and ensure consistent semantic identity across varying poses.

Main Methods:

  • Developed a multi-level fractional-order Gabor frequency-aware network for accurate extraction and reconstruction of high-frequency texture features.
  • Implemented a global semantic-pose alignment module using cross-modal attention for mapping pose features to appearance semantics.
  • Integrated these modules within a diffusion-based generative framework.

Main Results:

  • The proposed framework effectively reconstructs high-frequency texture details like hair strands and fabric wrinkles.
  • Achieved superior performance in maintaining structural integrity and natural textures under complex pose variations and large-angle rotations.
  • Demonstrated state-of-the-art results on DeepFashion and Market1501 datasets, validated by SSIM, FID, and perceptual quality metrics.

Conclusions:

  • The proposed diffusion-based framework significantly enhances texture fidelity and semantic consistency in pose-guided person image generation.
  • The integration of frequency awareness and global semantic alignment effectively overcomes limitations of previous approaches.
  • The method shows strong potential for realistic and consistent human image synthesis across diverse poses.