Towards Fine-Grained Human Pose Transfer With Detail Replenishing Network

Area of Science:

• Computer Vision
• Image Synthesis
• Machine Learning

Background:

• Human Pose Transfer (HPT) is vital for applications like fashion and virtual reality.
• Current HPT methods struggle with detail deficiency, content ambiguity, and style inconsistency, limiting visual realism.
• Existing approaches fail to meet the demands for high-fidelity appearance details in real-world applications.

Purpose of the Study:

• To address limitations in current HPT methods by introducing a more challenging Fine-grained Human Pose Transfer (FHPT) setting.
• To enhance semantic fidelity and detail replenishment in generated human images.
• To develop a robust methodology for generating realistic human images with preserved fine-grained details.

Main Methods:

• Developed a novel FHPT methodology combining content synthesis and feature transfer in a mutually-guided approach.
• Introduced a Detail Replenishing Network (DRN) and a coarse-to-fine training scheme.
• Established comprehensive fine-grained evaluation protocols for semantic analysis, structural detection, and perceptual quality.

Main Results:

• Achieved significant improvements over state-of-the-art methods on the DeepFashion dataset.
• Demonstrated 12%-14% gain in top-10 retrieval recall and 5% higher joint localization accuracy.
• Showcased a nearly 40% gain in face identity preservation, highlighting enhanced detail and consistency.

Conclusions:

• The proposed FHPT methodology and evaluation protocols effectively address the challenges of generating realistic human images with fine-grained details.
• The Detail Replenishing Network (DRN) significantly improves visual quality and semantic fidelity in human pose transfer.
• This work provides a strong foundation for advancing HPT in practical applications requiring high visual realism.