论文标题
神经头部重演具有潜在姿势描述
Neural Head Reenactment with Latent Pose Descriptors
论文作者
论文摘要
我们提出了一个神经头部重新制作系统，该系统由潜在姿势表示驱动，并能够预测RGB图像旁边的前景分割。潜在的姿势表示是整个重演系统的一部分，而学习过程仅基于图像重建损失。我们表明，尽管具有简单性，并且具有足够多的培训数据集，但这种学习成功地分解了身份的姿势。然后，由此产生的系统可以再现驾驶人员的模仿，此外，可以执行交叉人物重演。此外，我们表明，学到的描述符对于其他与姿势相关的任务（例如Kepoint预测和基于姿势的检索）很有用。
We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval.
