Estimating human gaze from natural eye images only is a challenging task. Gaze direction can be defined by the pupil- and the eyeball center where the latter is unobservable in 2D images. Hence, achieving highly accurate gaze estimates is an ill-posed problem. In this paper, we introduce a novel deep neural network architecture specifically designed for the task of gaze estimation from single eye input. Instead of directly regressing two angles for the pitch and yaw of the eyeball, we regress to an intermediate pictorial representation which in turn simplifies the task of 3D gaze direction estimation. Our quantitative and qualitative results show that our approach achieves higher accuracies than the state-of-the-art and is robust to variation in gaze, head pose and image quality.
Recent work in multi-person human pose estimation @cite_17 learns to estimate joint location heatmaps alongside so-called part affinity fields''. When combined, the two outputs then enable the detection of multiple peoples' joints with reduced ambiguity in terms of which person a joint belongs to. In addition, at the end of every image scale, the architecture concatenates feature maps from each separate stream such that information can flow between the part confidence'' and part affinity'' maps. Thus, they operate on the image representation space, taking advantage of the strengths of convolutional neural networks. Our work is similar in spirit in that it introduces a novel image-based representation.
@cite_17
2951856387
Human pose estimation and semantic part segmentation are two complementary tasks in computer vision. In this paper, we propose to solve the two tasks jointly for natural multi-person images, in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations. Specifically, we first train two fully convolutional neural networks (FCNs), namely Pose FCN and Part FCN, to provide initial estimation of pose joint potential and semantic part potential. Then, to refine pose joint location, the two types of potentials are fused with a fully-connected conditional random field (FCRF), where a novel segment-joint smoothness term is used to encourage semantic and spatial consistency between parts and joints. To refine part segments, the refined pose and the original part potential are integrated through a Part FCN, where the skeleton feature from pose serves as additional regularization cues for part segments. Finally, to reduce the complexity of the FCRF, we induce human detection boxes and infer the graph inside each box, making the inference forty times faster. Since there's no dataset that contains both part segments and pose labels, we extend the PASCAL VOC part dataset with human pose joints and perform extensive experiments to compare our method against several most recent strategies. We show that on this dataset our algorithm surpasses competing methods by a large margin in both tasks. This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem. The proposed PPN is favorably featured by low complexity and high accuracy of joint detection and partition. In particular, PPN performs dense regressions from global joint candidates within a specific embedding space, which is parameterized by centroids of persons, to efficiently generate robust person detection and joint partition. Then, PPN infers body joint configurations through conducting graph partition for each person detection locally, utilizing reliable global affinity cues. In this way, PPN reduces computation complexity and improves multi-person pose estimation significantly. We implement PPN with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor. Extensive experiments on benchmarks MPII Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF show the efficiency of PPN with new state-of-the-art performance. Analyzing joint movements of an athlete helps to improve the pose of the athlete. Human pose estimation (HPE) algorithms regress the locations of parts such as wrists, ankles and knees. In this paper, we propose a network that combines global and local information for HPE using a 2D image. Unlike previous works that have used global or local information separately, we use the combined information to enhance the performance of HPE. General information from a global network is used as an input to a local network to refine the location of a part using a variety of regions. The global network is based on ResNet-101 [6] and trained to regress a heatmap representing parts’ locations. The output features from the global network are used as input features for the local network. The local network learns spatial information using position sensitive score maps [11]. Through the end-to-end learning, the global network is affected by the local information. We demonstrate that the proposed HPE method is efficient on the LSP and UCF sports datasets. This paper aims at estimating full-body 3D human poses from monocular images of which the biggest challenge is the inherent ambiguity introduced by lifting the 2D pose into 3D space. We propose a novel framework focusing on reducing this ambiguity by predicting the depth of human joints based on 2D human joint locations and body part images. Our approach is built on a two-level hierarchy of Long Short-Term Memory (LSTM) Networks which can be trained end-to-end. The first level consists of two components: 1) a skeleton-LSTM which learns the depth information from global human skeleton features; 2) a patch-LSTM which utilizes the local image evidence around joint locations. The both networks have tree structure defined on the kinematic relation of human skeleton, thus the information at different joints is broadcast through the whole skeleton in a top-down fashion. The two networks are first pre-trained separately on different data sources and then aggregated in the second layer for final depth prediction. The empirical e-valuation on Human3.6M and HHOI dataset demonstrates the advantage of combining global 2D skeleton and local image patches for depth prediction, and our superior quantitative and qualitative performance relative to state-of-the-art methods.