论文标题
寿命年龄转化合成
Lifespan Age Transformation Synthesis
论文作者
论文摘要
我们解决了单照片年龄进步和回归的问题 - 对一个人将来的外观或过去的外观的预测。大多数现有的衰老方法仅限于改变质地，忽略了人类衰老和生长过程中发生的头部变化。这限制了以前方法对成年人衰老的适用性，而这些方法将这些方法应用于儿童照片不会产生质量的结果。我们提出了一种新型的多域图像到图像生成的对抗网络结构，该结构的潜在空间模拟了连续的双向老化过程。该网络经过FFHQ数据集的培训，我们将其标记为年龄，性别和语义分割。固定年龄类用作近似连续年龄变化的锚点。我们的框架可以从一张照片中预测0-70岁的全部肖像，从而修改头部的纹理和形状。我们在各种照片和数据集上展示了结果，并在最新情况下显示出显着改善。
We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation. Fixed age classes are used as anchors to approximate continuous age transformation. Our framework can predict a full head portrait for ages 0-70 from a single photo, modifying both texture and shape of the head. We demonstrate results on a wide variety of photos and datasets, and show significant improvement over the state of the art.
