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Perceiving Systems PS:License 1.0 3D Poses in the Wild Dataset The "3D Poses in the Wild dataset" is the first dataset with monocular hand-held video together with accurate 3D human poses for evaluation. Our method combines video and IMU to recover accurate 3D human body models and their projection into the video sequences. The dataset includes: 60 video sequences; 2D pose annotations; 3D poses obtained with our method; Camera poses for every frame in the sequences; 3D body scans; and 18 3D human models with different clothing variations.
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Perceiving Systems Software Workshop PS:License 1.0 Mesh Library When working in 3D graphics, one needs to load raw data, conduct various processing on it, visualize the results to help understanding, then save the output in different kinds of formats. Here we release the Mesh Library to facilitate all these aforementioned operations. This library is built on top of OpenGL and CGAL, with an easy-to-use Python interface. Other than the basic usages like data IO and interactive visualization, it also supports other more complex functionalities like texture rendering, visibility computation, and geometry arithmetic. We hope the release of this tool makes th...
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Perceiving Systems PS:License 1.0 SMALR: Capturing Animal Shape and Texture from Images The SMALR release includes an updated SMAL model of animals and 3D animal models recovered from images. SMALR is the Skinned Multi-Animal Linear Model with Refinement. All the 3D shapes from the CVPR paper are available for download as 3D meshes, which can be posed and animated. As we create new meshes, they will be added here.
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Software Workshop Perceiving Systems PS:License 1.0 Accelerated K-means An efficient and generic implementation of the k-means clustering algoriothm.
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Perceiving Systems PS:License 1.0 HMR: End-to-end Recovery of Human Shape and Pose Trained model to estimate 3D human shape and pose directly from an image. The input is pixels, and the output is a 3D body in SMPL format (shape parameters and pose parameters). Also provided is the code and data needed to train the model.
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Perceiving Systems PS:License 1.0 FLAME: 3D model of facial shape and expression FLAME is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. We provide the trained 3D face models, registrations for the dynamic D3DFACS dataset, and demo code in Chumpy and Tensorflow to load and sample the model, and to fit the model to 3D landmarks.
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Perceiving Systems PS:License 1.0 MANO: 3D hand model Data, code and model. This includes over 1000 3D hand scans and aligned meshes, the learned 3D hand shape model, the full articulated hand model with pose-dependent blend shapes. Also included is the SMPL body model with the hands attached to it, providing a realistic hand and body model.
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Perceiving Systems PS:License 1.0 BUFF: Bodies under Flowing Fashion, 4D dataset High quality 4D dataset of people in clothing with ground truth 3D shape. The BUFF dataset consists of 5 subjects, 3 male and 2 female wearing 2 clothing styles: a) t-shirt and long pants and b) a soccer outfit. They perform 3 different motions i) hips ii) tilt_twist_left iii) shoulders_mill.
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Perceiving Systems PS:License 1.0 Dynamic FAUST This dataset is a unique resource containing over 40,000 4D scans of multiple people; 4D means 3D scans over time. Processing 4D data is challenging, so we provide aligned data in which we have registered a common template mesh to all scans. This alignment process takes into account geometry and surface texture to make it accurate. The dataset includes the raw scan data, registered template meshes, and masks that say where the template mesh is sufficiently accurate to be considered ground truth.
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Perceiving Systems PS:License 1.0 SMAL: 3D articulated model of animals shapes We provide the SMAL model of animal shapes and demo code. We also provide all the results from the CVPR paper of animal shapes estimated from images. We do not provide the 3D scans of the toy animals for copyright reasons but do provide a shopping list so that you can purchase the same toys that we used.
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Perceiving Systems PS:License 1.0 SURREAL: Synthetic human dataset and trained networks First large-scale person dataset to generate depth, body parts, optical flow, 2D/3D pose, surface normals ground truth for RGB video input. The dataset contains 6M frames of synthetic humans. The images are photo-realistic renderings of people under large variations in shape, texture, view-point and pose. To ensure realism, the synthetic bodies are created using the SMPL body model, whose parameters are fit by the MoSh method given raw 3D MoCap marker data. Trained CNNs are also provided.
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Perceiving Systems PS:License 1.0 Unite the People – Closing the Loop Between 3D and 2D Human Representations The dataset includes annotations of common human pose datasets. These include 3D body pose, 91 surface and joint landmarks, foreground segmentation, and body part segments. Together with the images, these can be used to train neural networks for human pose estimation tasks, including 3D pose estimation. The 3D body is represented by SMPL. Training code is provided.
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Perceiving Systems PS:License 1.0 MR-Flow: Optical flow in mostly-rigid scenes Code for the paper "Optical Flow in Mostly Rigid Scenes" by Jonas Wulff, Laura Sevilla-Lara, Michael Black, CVPR 2017. This is one of the best performing methods across different datasets. In rigid parts of the scene, a plane-plus-parallax model is used. The method segments out the non-rigid regions and uses a more generic flow method there.
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Perceiving Systems PS:License 1.0 A Generative Model of People in Clothing We provide an image-based generative model of people in clothing for the full body. The training dataset is built on top of Chictopia10K. We provide processed annotations as well as the SMPL body model fit to the images. We also provide our trained models for download.
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Perceiving Systems Autonomous Vision PS:License 1.0 Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We ...
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Software Workshop Perceiving Systems PS:License 1.0 Scan Manager A web application for being able to manage the increasing amount of body scans.
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Perceiving Systems PS:License 1.0 SPyNet: Optical Flow Estimation using a Spatial Pyramid Network We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Check the website for updates; we provide code for the original SypNet as well as an end-to-end trainable version.
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Perceiving Systems PS:License 1.0 Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects Webpages for the GCPR 2013, GCPR 2014, ICCV 2015, IJCV 2016, ECCVw 2016 papers. The data contains: (IJCV 2016, GCPR 2014) annotated RGB-D and multicamera-RGB dataset of one or two hands interacting with each other and/or with a rigid or an articulated object, (ICCV 2015) RGB-D dataset of a hand rotating a rigid object for 3d scanning, (GCPR 2013) synthetic dataset of two hands interacting with each other, (ECCVw 2016) RGB-D dataset of an object under manipulation.
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Perceiving Systems PS:License 1.0 SMPLify: 3D human pose and shape estimation from a single image Given a single image, extract the 3D SMPL pose and shape parameters. We provide a Python demo code needed to run SMPLify. We also provide results from the ECCV paper for comparison. For all the datasets we used (LSP, HumanEva-I, Human3.6M) we provide the detected joints and our results as SMPL model parameters and as a mesh (vertices and faces). The code package includes an example script showing how to load results. Please see the README in the code package and the FAQ.
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Perceiving Systems PS:License 1.0 BodyTalk: Tool that relates 3D body shape to words This website provides a tool to explore 3D body shape and linguistic descriptions of shape. We provide a set of shape sliders and linguistic sliders that can be used to change body shape. This allows you to explore how people think about body shape and how shape and adjectives are correlated.
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Perceiving Systems PS:License 1.0 Semantic Optical Flow Data and code necessary to reproduce results from the CVPR 2016 paper on semantic optical flow. Semantic scene segmentation enables different flow models to be used in different regions and then composed using a locally layered approach.
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Perceiving Systems PS:License 1.0 Video segmentation via object flow Matlab implementation of the paper Video Segmentation via Object Flow Yi-Hsuan Tsai, Ming-Hsuan Yang and Michael J. Black IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
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Perceiving Systems PS:License 1.0 SMPL: A skinned multi-person linear body model SMPL is like a PDF format for 3D bodies. It is a realistic 3D model of the human body that is based on blend skinning and blend shapes that is learned from thousands of 3D body scans. It is fully portable, works with many existing game engines and is useful for computer vision. This site provides resources to learn about SMPL, including example FBX files with animated SMPL models, and code for using SMPL in Python, Maya and Unity. The Python code shows how to use SMPL in computer vision problems. Maya and Unity scripts help set up the model for animation in these 3D environments. We p...
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Perceiving Systems PS:License 1.0 MoSh: Motion and Shape Capture from Sparse Markers The original MoSh dataset is available as well as code for the latest MoSh++ method. Marker-based motion capture (mocap) is widely criticized as producing lifeless animations. MoSh (Motion and Shape capture), automatically extracts detail present in the original mocap maker data. MoSh estimates body shape and pose together using sparse marker data by exploiting a parametric model of the human body. The dataset contains: 1) The original .c3d files with MOCAP marker-data. 2) Estimated 3D shape meshes. 3) 3D scans from a high resolution scanner for comparison. The code is the latest MoSh++ m...
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Perceiving Systems PS:License 1.0 Dyna: 4D meshes of dynamic human soft tissue motion To look human, digital full-body avatars need to have soft tissue deformations like those of real people. Current methods for physics simulation of soft tissue lack realism, are computationally expensive, or are hard to tune. Learning soft tissue motion from example, however, has been limited by the lack of dense, high-resolution, training data. We address this using a 4D capture system and a method for accurately registering 3D scans across time to a template mesh. Using over 40,000 scans of ten subjects, we compute how soft tissue motion causes mesh triangles to deform relative to a base ...
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Perceiving Systems PS:License 1.0 Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction (code and data) This release contains code and data. Most mocap datasets are too small or to constrained to capture the full range of human motions. In particular, they are too small to explore joint angle limits. Here we provide a mocap dataset in which the subjects are gymnasts who are able to explore a wide range of human poses. The dataset allows one to develop pose priors that obey these limits and to model how these joint limits actually vary with pose. We include code to learn joint angle limits and to estimate 3D pose from 2D joint locations.
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Perceiving Systems Autonomous Vision PS:License 1.0 KITTI 2015: Stereo, Flow, and Scene Flow Benchmark KITTI is one of the most popular datasets for evaluation of vision algorithms, particuarly in the context of street scenes and autonomous driving. The stereo 2015 / flow 2015 / scene flow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks, it comprises dynamic scenes for which the ground truth has been established in a semi-automatic process.
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Perceiving Systems PS:License 1.0 PCA-Flow: Fast, approximate optical flow computation This software package contains two algorithms for the computation of optical flow, as described in Wulff & Black, "Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers" (CVPR 2015). PCA-Flow computes approximate optical flow extremely quickly, by making the assumption that optical flow lies on a low-dimensional subspace. PCA-Layers extends this to a layered model to increase accuracy, especially at boundaries. It is the most accurate layered model on the MPI Sintel dataset.
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Perceiving Systems PS:License 1.0 The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose The Stitched Puppet (SP) is a realistic part-based 3D body model of the human body. It offers the best features of part-based body models used in Computer Vision and statistical body models used in Computer Graphics. The release includes data and code to fit the SP model to 3D scans.
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Perceiving Systems PS:License 1.0 OpenDR: An open differentiable renderer The OpenDR, is the first open source differentiable renderer. It provides a simple Python interface for defining an objective function with a forward generative process and then automatically differentiating and optimizing this. OpenDR allows for quick design and testing of generative models in computer vision. The code provides examples. OpenDR has been widely use.
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Perceiving Systems PS:License 1.0 FAUST dataset: High-resolution 3D scans with ground truth correspondence FAUST contains 300 real, high-resolution human scans of 10 different subjects in 30 different poses, with automatically computed ground-truth correspondences. We provide a training set with scans and ground truth correspondence. We also provide a separate test set of scans with an evaluation website that compares results of mesh correspondence.
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Perceiving Systems PS:License 1.0 Secrets of Optical Flow: Code for various methods Matlab code for robust optical flow -- Classic++ and Classic-NL -- as described in the IJCV paper "A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them". This code is widely used as a baseline and starting point for "classical" flow methods. Matlab version of the "Black and Anandan" robust flow method: https://deqings.github.io/public_files/ba.zip Matlab version of "Horn and Schunck": https://deqings.github.io/public_files/hs.zip Original implementation from CVPR'2010 paper: https://deqings.github.io/public_files/cvpr10_flow_code.zip
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Perceiving Systems PS:License 1.0 Flowing Puppets Code for ICCV'13 paper on "Estimating Human Pose with Flowing Puppets". This addresses the problem of upper-body human pose estimation in uncontrolled monocular video sequences, without manual initialization. The "flowing puppets" model provide integrates image evidence across frames to improve pose inference. We provide the code used for the experiments in the paper. We also provide the "puppet flow" annotation tool.
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Perceiving Systems PS:License 1.0 JHMDB: Joint-annotated Human Motion Data Base A fully annotated data set for human actions and human poses. It is based on the HMDB human motion dataset but includes optical flow on the person, the segmentation of the person, joint locations, action labels, and meta data.
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Perceiving Systems PS:License 1.0 BMI Visualizer This website helps people understand body mass index through a novel visualization of 3D body shape. Enter height and weight to see a 3D body shape with these properties and see the corresponding BMI. Move a slider to change BMI and see how body shape changes.
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Perceiving Systems Autonomous Vision PS:License 1.0 The KITTI Dataset The KITTI dataset is the de-facto standard for developing and testing computer vision algorithms for real-world autonomous driving scenarios and more.
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Perceiving Systems PS:License 1.0 Sintel Optical Flow Dataset and Benchmark The MPI Sintel Dataset is one of the most widely used datasets for training and evaluating optical flow algorithms. It is the first synthetic dataset to achieve wide use because of it well represents natural scenes and motions. It is also extremely challenging and current methods have still not fully "solved" the problem of flow estimation for Sintel. Sintel is designed to encourage research on long-range motion, motion blur, multi-frame analysis, non-rigid motion. Algorithms are evaluated on held-out test data and results are displayed for comparison. The dataset contains flow field...
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Perceiving Systems PS:License 1.0 Lie Bodies This code supports the core representation needed for Lie Bodies as described in Freifeld and Black, ECCV 2012. Currently this is only a partial version of what is presented in the paper. The code takes pairs of triangles and computes the "Q" matrices and the corresponding (R,A,S) decompositions that are the foundation of Lie Bodies (see paper for details).
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Perceiving Systems PS:License 1.0 Body Shape Visualizer This web-based tool lets users enter information about body measurements (height, waist, inseam, etc) and visualize a 3D body shape that corresponds to these measurements.
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Perceiving Systems PS:License 1.0 Middlebury Optical Flow Dataset and Benchmark The Middlebury flow dataset has been a de-facto standard in the field since 2007. The dataset introduced several innovations. It is the first dataset to contain real image sequences with independent motions, and ground truth optical flow. Second, it provides realistically rendered synthetic scenes with ground truth flow. It also includes a frame interpolation task using real video sequences. While, by today's standards, the dataset is small and the sequences somewhat simple, it remains a useful tool for evaluating the generality of optical flow methods.
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Perceiving Systems PS:License 1.0 HumanEva datasets I and II HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. This is the repository for the widely use HumanEva dataset. This was the first dataset to include mutii-camera video capture of people with ground truth 3D human pose. It established the quantitative evaluation of human pose estimation using well-defined metrics in 2D and 3D. The dataset was developed at Brown University and is hosted by MPI.
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Perceiving Systems PS:License 1.0 Archival Image sequences This is a collection of images sequences used in the 1990's and early 2000's. It includes the Yosemite sequences, Flower garden, and several others.
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Perceiving Systems PS:License 1.0 Robust dense optical flow estimation This is the original C implementation of the "Black and Anandan" optical flow algorithm. For a Matlab version that is written by Deqing Sun and much better see: https://deqings.github.io/public_files/ba.zip
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Perceiving Systems PS:License 1.0 Robust area-based optical flow estimation This is the original C version of the robust area-based flow code from Michael Black's thesis.
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