Video-based human pose transfer is a video-to-video generation task that animates a plain source human image based on a series of target human poses. Considering the difficulties in transferring highly structural patterns on the garments and discontinuous poses, existing methods often generate unsatisfactory results such as distorted textures and flickering artifacts. To address these issues, we propose a novel Deformable Motion Modulation (DMM) that utilizes geometric kernel offset with adaptive weight modulation to simultaneously perform feature alignment and style transfer. Different from normal style modulation used in style transfer, the proposed modulation mechanism adaptively reconstructs smoothed frames from style codes according to the object shape through an irregular receptive field of view. To enhance the spatio-temporal consistency, we leverage bidirectional propagation to extract the hidden motion information from a warped image sequence generated by noisy poses. The proposed feature propagation significantly enhances the motion prediction ability by forward and backward propagation. Both quantitative and qualitative experimental results demonstrate superiority over the state-of-the-arts in terms of image fidelity and visual continuity. The source code is publicly available at github.com/rocketappslab/bdmm.

翻译：视频人体姿态迁移是一项视频到视频的生成任务，旨在基于一系列目标人体姿态对静态源人体图像进行动画化。针对服装上高度结构化图案的迁移以及非连续姿态带来的挑战，现有方法常产生纹理失真和闪烁伪影等不理想结果。为解决这些问题，我们提出一种新颖的可变形运动调制（DMM），该方法利用几何卷积核偏移与自适应权重调制，同步实现特征对齐与风格迁移。与风格迁移中使用的常规风格调制不同，所提出的调制机制通过不规则感受野根据物体形状从风格编码中自适应重建平滑帧。为增强时空一致性，我们利用双向传播从噪声姿态生成的扭曲图像序列中提取隐式运动信息。这种特征传播通过前向与反向传播显著提升了运动预测能力。定量与定性实验结果均表明，该方法在图像保真度和视觉连续性方面优于现有最先进技术。源代码已开源：github.com/rocketappslab/bdmm。