论文标题
3D面部重建具有密集地标
3D face reconstruction with dense landmarks
论文作者
论文摘要
地标通常在面部分析中起关键作用，但是仅稀疏的地标就不能代表身份或表达的许多方面。因此，为了更准确地重建面，地标通常与其他信号（如深度图像或技术）等其他信号结合使用。我们可以通过使用更多地标使事情变得简单吗？在答案中，我们提出了第一种准确地预测10倍地标的方法的方法，覆盖了整个头部，包括眼睛和牙齿。这是使用合成培训数据来完成的，该数据保证了完美的地标注释。通过将可变形的模型拟合到这些密集的地标上，我们在野外实现了单眼3D面重建的最新结果。我们表明，密集的地标是通过在单眼和多视图方案中展示准确和表现力的面部绩效捕获来整合跨帧面部形状信息的理想信号。这种方法也非常有效：我们可以预测密集的地标，并在单个CPU线程上以超过150fps的速度适合我们的3D面模型。请参阅我们的网站：https：//microsoft.github.io/denselandmarks/。
Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces more accurately, landmarks are often combined with additional signals like depth images or techniques like differentiable rendering. Can we keep things simple by just using more landmarks? In answer, we present the first method that accurately predicts 10x as many landmarks as usual, covering the whole head, including the eyes and teeth. This is accomplished using synthetic training data, which guarantees perfect landmark annotations. By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild. We show that dense landmarks are an ideal signal for integrating face shape information across frames by demonstrating accurate and expressive facial performance capture in both monocular and multi-view scenarios. This approach is also highly efficient: we can predict dense landmarks and fit our 3D face model at over 150FPS on a single CPU thread. Please see our website: https://microsoft.github.io/DenseLandmarks/.
