通过双重任务相关性和亲和学习进行姿势引导人形象生成
IEEE transactions on visualization and computer graphics
|June 15, 2023
概括
这项研究引入了一种双任务姿势变换器网络和纹理亲和 (DPTN-TA) 以改善姿势指导人的图像生成,通过解决不良姿势问题和纹理映射. 这种新的方法增强了产生的图像中的现实主义和细节,即使有显著的姿势变化.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像生成 图像生成
背景情况:
- 姿势引导人形象生成 (PGPIG) 旨在在姿势之间转换人形象.
- 现有的方法与PGPIG的不良性质作斗争,需要更好的纹理映射监督.
- 这些挑战限制了生成图像的现实主义和细节,特别是在显著的姿势变化下.
研究的目的:
- 提出一种新的方法,双任务姿势变换器网络和纹理亲和度 (DPTN-TA),以解决PGPIG的局限性.
- 改进与准确的姿势转换感知现实的人形图像的生成.
- 为了增强纹理映射监督和图像生成期间的细节保存.
主要方法:
- 引入了一种双重任务的姿势变压器网络和纹理亲和力 (DPTN-TA) 机制.
- 加入了一个辅助的源到源任务,使用一个语结构来帮助错误的源到目标学习.
- 开发了一种姿势转换器模块 (PTM) 用于细粒度映射和纹理传输,以及一种新的纹理亲和力损失.
主要成果:
- DPTN-TA有效地产生感知上现实的人形图像,具有显著的姿势变化.
- 该方法在LPIPS和FID指标方面表现出优于最先进的方法的性能.
- 成功扩展到其他对象的视图合成,如面孔和椅子,展示了多功能性.
结论:
- 通过应对固有的挑战,DPTN-TA为姿势引导人图像生成提供了一个强大的解决方案.
- 拟议的双任务学习和纹理亲和机制显著提高了图像的细节和现实性.
- 该模型的适应性超越了人体数字,表明它在计算机视觉任务中具有广泛的适用性.
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