Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation

Author:

Tang Jilin,Yuan Yi,Shao Tianjia,Liu Yong,Wang Mengmeng,Zhou Kun

Abstract

In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance. Given the inefficiency of standard CNNs in handling large spatial transformation, we propose a structure-aware flow based method for high-quality person image generation. Specifically, instead of learning the complex overall pose changes of human body, we decompose the human body into different semantic parts (e.g., head, torso, and legs) and apply different networks to predict the flow fields for these parts separately. Moreover, we carefully design the network modules to effectively capture the local and global semantic correlations of features within and among the human parts respectively. Extensive experimental results show that our method can generate high-quality results under large pose discrepancy and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Human image animation via semantic guidance;Computers & Graphics;2024-02

2. Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

3. WaveIPT: Joint Attention and Flow Alignment in the Wavelet domain for Pose Transfer;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

4. Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

5. CPD-GAN: Cascaded Pyramid Deformation GAN for Pose Transfer;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

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