3D Facial Landmark Localization

AFLW2000-3D is a dataset of 2000 images that have been annotated with image-level 68-point 3D facial landmarks. This dataset is used for evaluation of 3D facial landmark detection models. The head poses are very diverse and often hard to be detected by a CNN-based face detector. Source: https://www.tensorflow.org/datasets/catalog/aflw2k3d Image Source: http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm

3DFAW contains 23k images with 66 3D face keypoint annotations. Source: Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild Image Source: http://mhug.disi.unitn.it/workshop/3dfaw/

Human3.6M 3D WholeBody (H3WB) is a large scale dataset with 133 whole-body keypoint annotations on 100K images, made possible by a new multi-view pipeline. It is designed for the three new tasks : i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, iii) 3D whole-body pose estimation from a single RGB image. Source: H3WB: Human3.6M 3D WholeBody Dataset and Benchmark Image Source: https://arxiv.org/pdf/2211.15692v1.pdf

DAD-3DHeads dataset consists of 44,898 images collected from various sources (37,840 in the training set, 4,312 in the validation set, and 2,746 in the test set). The dataset is well-balanced over a wide range of poses, face expressions, and occlusions. The attribute labels are a valuable signal for subgroup analysis and for generalisation to in-the-wild deployment conditions.

Urban is one of the most widely used hyperspectral data used in the hyperspectral unmixing study. There are 307x307 pixels, each of which corresponds to a 2x2 m2 area. In this image, there are 210 wavelengths ranging from 400 nm to 2500 nm, resulting in a spectral resolution of 10 nm. After the channels 1-4, 76, 87, 101-111, 136-153 and 198-210 are removed (due to dense water vapor and atmospheric effects), we remain 162 channels (this is a common preprocess for hyperspectral unmixing analyses). There are three versions of ground truth, which contain 4, 5 and 6 endmembers respectively, which are introduced in the ground truth. Linda S. Kalman and Edward M. Bassett III "Classification and material identification in an urban environment using HYDICE hyperspectral data", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.283843 Hosted at: - https://rslab.ut.ac.ir/data - http://lesun.weebly.com/hyperspectral-data-set.html - https://erdc-library.erdc.dren.mil/jspui/handle/11681/2925

Tables of the blendshapes from a group of the images of the FER2013 dataset, generated using MediaPipe library, based on the ARKit face blendshapes. with classes of the images in a separate column, describing the categories Happy, Unknown, Sad.