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Register a new userFace-from-Depth for Head Pose Estimation on Depth Images
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Added by
Guido Borghi
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Depth cameras allow to set up reliable solutions for people monitoring and behavior understanding, especially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only. A head detection and localization module is also included, in order to develop a complete end-to-end system. The core element of the framework is a Convolutional Neural Network, called POSEidon+, that receives as input three types of images and provides the 3D angles of the pose as output. Moreover, a Face-from-Depth component based on a Deterministic Conditional GAN model is able to hallucinate a face from the corresponding depth image. We empirically demonstrate that this positively impacts the system performances. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Experimental results show that our method overcomes several recent state-of-art works based on both intensity and depth input data, running in real-time at more than 30 frames per second.
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Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images. State-of-the-art estimators ad-dress this problem by regularizing3D hand pose estimationmodels during training to enforce the consistency betweenthe predicted3D poses and the ground-truth depth maps.However, these estimators rely on both RGB images and thepaired depth maps during training. In this study, we proposea conditional generative adversarial network (GAN) model,called Depth-image Guided GAN (DGGAN), to generate re-alistic depth maps conditioned on the input RGB image, anduse the synthesized depth maps to regularize the3D handpose estimation model, therefore eliminating the need forground-truth depth maps. Experimental results on multiplebenchmark datasets show that the synthesized depth mapsproduced by DGGAN are quite effective in regularizing thepose estimation model, yielding new state-of-the-art resultsin estimation accuracy, notably reducing the mean3D end-point errors (EPE) by4.7%,16.5%, and6.8%on the RHD,STB and MHP datasets, respectively.
In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation. Instead of accurate 3D positions, the depth ranking can be identified by human intuitively and learned using the deep neural network more easily by solving classification problems. Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D pose regression in two-stage methods from being ill-posed. In our method, firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to extract depth rankings of human joints from images. Secondly, a coarse-to-fine 3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth rankings and 2D human joint locations. Additionally, to improve the generality of our model, we introduce a statistical method to augment depth rankings. Our approach outperforms the state-of-the-art methods in the Human3.6M benchmark for all three testing protocols, indicating that depth ranking is an essential geometric feature which can be learned to improve the 3D pose estimation.
While convolutional neural networks (CNNs) have significantly boosted the performance of face related algorithms, maintaining accuracy and efficiency simultaneously in practical use remains challenging. Recent study shows that using a cascade of hourglass modules which consist of a number of bottom-up and top-down convolutional layers can extract facial structural information for face alignment to improve accuracy. However, previous studies have shown that features produced by shallow convolutional layers are highly correspond to edges. These features could be directly used to provide the structural information without addition cost. Motivated by this intuition, we propose an efficient multitask face alignment, face tracking and head pose estimation network (ATPN). Specifically, we introduce a shortcut connection between shallow-layer features and deep-layer features to provide the structural information for face alignment and apply the CoordConv to the last few layers to provide coordinate information. The predicted facial landmarks enable us to generate a cheap heatmap which contains both geometric and appearance information for head pose estimation and it also provides attention clues for face tracking. Moreover, the face tracking task saves us the face detection procedure for each frame, which is significant to boost performance for video-based tasks. The proposed framework is evaluated on four benchmark datasets, WFLW, 300VW, WIDER Face and 300W-LP. The experimental results show that the ATPN achieves improved performance compared to previous state-of-the-art methods while having less number of parameters and FLOPS.
To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detection and temporal tracking that are combined seamlessly for robotic perception and interaction as well as Augmented Reality (AR). A separate evaluation of, respectively, the object detection and the temporal tracker demonstrates the important stride in research as well as the impact on industrial robotic applications and AR. When evaluated on a standard dataset, the detector produced the highest f1-score with a large margin while the tracker generated the best accuracy at a very low latency of approximately 2 ms per frame with one CPU core: both algorithms outperforming the state of the art. When combined, we achieve a powerful framework that is robust to handle multiple instances of the same object under occlusion and clutter while attaining real-time performance. Aiming at stepping beyond the simple scenarios used by current systems, often constrained by having a single object in absence of clutter, averting to touch the object to prevent close-range partial occlusion, selecting brightly colored objects to easily segment them individually or assuming that the object has simple geometric structure, we demonstrate the capacity to handle challenging cases under clutter, partial occlusion and varying lighting conditions with objects of different shapes and sizes.
Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation code. We summarize important conclusions here: (1) Pose estimation appears roughly solved for scenes with isolated hands. However, methods still struggle to analyze cluttered scenes where hands may be interacting with nearby objects and surfaces. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.
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