FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
By Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, Xun Cao. Computer Vision and Pattern Recognition, 2020
372 citations recorded by the source corpus.
In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. The unprecedented dataset and code will be released to public for research purpose.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/0eefdb597c1bb28ec06bcd66fd4e10e19c5c2fa2
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In Computer Vision and Pattern Recognition
- Michael J. Black
- Timo Bolkart
- Otmar Hilliges
- Haiwen Feng
- Radek Daněček
- Yao Feng
- Juyong Zhang
- Bo Peng
Citations in the corpus
- EMOCA: Emotion Driven Monocular Face Capture and Animation (2022, background): “2 [98] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao.”
- HeadNeRF: A Realtime NeRF-based Parametric Head Model (2021, methodology): “Early works of the parametric face/head model [3, 6, 11, 30, 41, 57] mainly model 3D faces with the topologically uniformed face template mesh and usually ignore to represent the non-face parts, such as hair and teeth.”
- Learning an animatable detailed 3D face model from in-the-wild images (2020, methodology): “2020] either use detailed 3D scans as training data and, hence, do not generalize to unconstrained images [Yang et al. 2020], or model expression-dependent details as part of the appearance map rather than the geometry [Chaudhuri et al.”
- I M Avatar: Implicit Morphable Head Avatars from Videos (2021, methodology): “To represent detailed geometry, a few methods additionally reconstruct a layer of mid- or fine-level correctives that can be deformed along with the underlying coarse mesh [17, 19, 22, 58].”