They operate by projecting light in a pattern, usually in the form of multiple parallel beams, onto an object. Artec's 3D scanners are structured light scanners. If you use any of the resources provided on this page in any of your publications we ask you to cite the following work.3D scanners capture the geometry of an object and produce a three-dimensional digital model. The labels are split into two parts, as some depth images are noisy not all of them could have been used for training, the labels for clean images can be found here, while the labels for all images are here. We hand labeled a subset of images on from this dataset. NON RIGID ALIGNMENT ARTEC STUDIO PATCHThis allowed us to train our CLM-Z patch experts using both the texture and depth images. We converted this dataset into separate images and generated depth images for them. The database contains 606 3D facial expression sequences captured from 101 subjects. The 3D facial expressions are captured at a video rate (25 frames per second). This is a high-resolution 3D dynamic facial expression database. Indices can be found here.Īs a static dataset we used BU-4DFE. We also took and hand labeled a subset of the Biwi dataset for facial feature points, in order to evaluate our facial tracking, The labels can be found here. NON RIGID ALIGNMENT ARTEC STUDIO CODETo run our code on this dataset it first needs to be converted to videos and the depth images have to be aligned using the provided calibration data. For each frame, a depth image, the corresponding rgb image (both 640x480 pixels), and the annotation is provided. The dataset contains over 15K images of 20 people. However, it is more difficult for temporal trackers due to many missing frames, and very large pose variations. This is a similar dataset to that of ICT-3DHP. They will be slightly different from those reported in the paper, due to several fixes and additional robustness tweaks.Īs an additional benchmark we used the Biwi Kinect Head Pose Database. The expected results are made available with the code. To run our tracker on the dataset one can just use the Matlab script code provided with the code. To run our code on this dataset, simply change the expected database location in the provided Matlab scripts and run them.įor a purely intensity based benchmark we use the Boston University head poseĭataset which can be dowloaded here. In addition to colour videos it has a per frame depth map captured using Microsoft Kinect. ICT-3DHP is a video dataset of ground truth labeled head poses. However that code is not cleaned up and would be slightly more difficult to follow. The exact code that was run in the paper can also be made available upon request. The code also contains experiment running scripts, with the results to expect from running the code on the publicly available datasets used in the paper and described below. Additionally, failure criteria were added allowing the CLM and CLM-Z tracking to reinitialise when tracking is lost. This is mainly due to the use of an extended training model to that described in the paper. Therefore, the results of running the code on the datasets used in the paper are slightly those reported in the paper (better in most cases). However, it contains several slight tweaks added for robustness and performance. NON RIGID ALIGNMENT ARTEC STUDIO DOWNLOADThe code available for download uses the approaches described in the paper. Source (C++ Visual Studio Project, with Matlab scripts to run experiments), please email the author for access.
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