用草图生成带表情的3D人脸模型，结合神经网络与轮廓优化。
Generating 3D models from sketches of human faces using a combined approach of Convolutional Neural Networks, Procedural Modeling, and Contour Mapping
- 通过CNN识别草图中的表情动作单元（FACS）
- 将检测到的表情映射到3D人脸模型（Valley Girl）上
- 用主动蛇形轮廓填补模型与草图间的间隙，提升匹配度
从人脸草图生成3D模型是计算机图形学中的活跃研究方向，可显著简化专业与新手艺术家的人脸建模流程。受面部表情显著改变轮廓的启发，本文提出一种融合表情检测与3D建模的新方法。该方法包含三个组件：卷积神经网络（CNN）、参数化3D人脸模型（Valley Girl）和主动蛇形轮廓（Active Snake Contours）。首次在文献中，使用自建数据集训练CNN以检测草图中的表情动作单元（FACS），并将该表情复制至Valley Girl模型生成对应表情的3D人脸。随后，利用主动蛇形轮廓计算模型与草图之间的变换，填补轮廓间隙，实现高保真重建。
原文摘要 · Abstract (English)
Generating 3D models from face sketches is an active topic of research in Computer Graphics due to its potential to tremendously facilitate the modeling of faces for both professional 3D arists and novices. Motivated by the observation that facial expressions are responsible for significantly altering and shaping the contours in our faces, we combine both expression detection and 3D model generation in our approach. The result is a novel approach to generating 3D models from sketches which relies on three components: Convolutional Neural Networks, a parametric 3D face model (Valley Girl), and Active Snake Contours. For the first time in the literature, CNNs are trained (using our own generated dataset) to detect the expression in the given sketch through detecting the active FACS Action Units. The expression is then duplicated on Valley Girl to obtain a 3D model with a similar expression. Active Snake Contours are then used to find the transforms needed to close the gaps between that model and the given sketch.
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