Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for production quality and user engagement. However, existing HPT methods often suffer from three fundamental issues: detail deficiency, content ambiguity and style inconsistency, which severely degrade the visual quality and realism of generated images. Aiming towards real-world applications, we develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment. Concretely, we analyze the potential design flaws of existing methods via an illustrative example, and establish the core FHPT methodology by combing the idea of content synthesis and feature transfer together in a mutually-guided fashion. Thereafter, we substantiate the proposed methodology with a Detail Replenishing Network (DRN) and a corresponding coarse-to-fine model training scheme. Moreover, we build up a complete suite of fine-grained evaluation protocols to address the challenges of FHPT in a comprehensive manner, including semantic analysis, structural detection and perceptual quality assessment. Extensive experiments on the DeepFashion benchmark dataset have verified the power of proposed benchmark against start-of-the-art works, with 12\%-14\% gain on top-10 retrieval recall, 5\% higher joint localization accuracy, and near 40\% gain on face identity preservation. Moreover, the evaluation results offer further insights to the subject matter, which could inspire many promising future works along this direction.
翻译:人类变形(HPT)是一个新兴的研究课题,在时装设计、媒体制作、在线广告和虚拟现实方面潜力巨大。对于这些应用而言,精细外观细节的视觉现实性对于生产质量和用户参与至关重要。然而,现有的HPT方法往往存在三个根本性问题:细节不足、内容模糊和风格不一致,严重降低了所产生图像的视觉质量和现实性。我们的目标是实现现实世界应用,我们开发了一个更具挑战性的、更实用的HPT设置,称为精美的人类变形(FHPT),更侧重于语义上的忠诚和详细补充。具体地说,我们通过一个示例实例分析现有方法的潜在设计缺陷,并通过相互指导的方式梳理内容合成和特征转换的理念来确立FHPT方法的核心方法。我们用一个详尽的重印网络(DRN)和一个相应的粗度至平面的模型培训计划来证实拟议方法。此外,我们进一步建立一套精准的评估协议,以便应对FPT的更高准确性和详细补给性补充。我们未来在基础测试中进行的结构质量分析。