Papers › Region-adaptive Texture Enhancement for Detailed Person Image Synthesis
Region-adaptive Texture Enhancement for Detailed Person Image Synthesis
Lingbo Yang, Pan Wang, Xinfeng Zhang, Shanshe Wang, Zhanning Gao, Peiran Ren, Xuansong Xie, Siwei Ma, Wen Gao
The ability to produce convincing textural details is essential for the fidelity of synthesized person images. However, existing methods typically follow a ``warping-based'' strategy that propagates appearance features through the same pathway used for pose transfer. However, most fine-grained features would be lost due to down-sampling, leading to over-smoothed clothes and missing details in the output images. In this paper we presents RATE-Net, a novel framework for synthesizing person images with sharp texture details. The proposed framework leverages an additional texture enhancing module to extract appearance information from the source image and estimate a fine-grained residual texture map, which helps to refine the coarse estimation from the pose transfer module. In addition, we design an effective alternate updating strategy to promote mutual guidance between two modules for better shape and appearance consistency. Experiments conducted on DeepFashion benchmark dataset have demonstrated the superiority of our framework compared with existing networks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Pose Transfer | Deep-Fashion | RATE | FID | 14.611 | #4 of 12 | Archive leaderboard | report |
| Pose Transfer | Deep-Fashion | RATE | IS | 3.125 | #4 of 12 | Archive leaderboard | report |
| Pose Transfer | Deep-Fashion | RATE | LPIPS | 0.218 | #4 of 12 | Archive leaderboard | report |
| Pose Transfer | Deep-Fashion | RATE | Retrieval Top10 Recall | 30.89 | #4 of 12 | Archive leaderboard | report |
| Pose Transfer | Deep-Fashion | RATE | SSIM | 0.774 | #4 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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