Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These limitations are addressed by the recent VIS transformer (VisTR) [25] which performs VIS end-to-end within a clip. However, VisTR suffers from long training time due to its frame-wise dense attention. In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips. This paper proposes EfficientVIS, a fully end-to-end framework with efficient training and inference. At the core are tracklet query and tracklet proposal that associate and segment regions-of-interest (RoIs) across space and time by an iterative query-video interaction. We further propose a correspondence learning that makes tracklets linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS requires$15\times$fewer training epochs while achieving state-of-the-art accuracy on the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance segmentation in a single end-to-end pass without data association at all.

In this paper, we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks (CNNs). Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information. Our proposed 3D CNN-based method, taking a 3D volumetric representation of the hand depth image as input and extracting 3D features from the volumetric input, can capture the 3D spatial structure of the hand and accurately regress full 3D hand pose in a single pass. In order to make the 3D CNN robust to variations in hand sizes and global orientations, we perform 3D data augmentation on the training data. To further improve the estimation accuracy, we propose applying the 3D deep network architectures and leveraging the complete hand surface as intermediate supervision for learning 3D hand pose from depth images. Extensive experiments on three challenging datasets demonstrate that our proposed approach outperforms baselines and state-of-the-art methods. A cross-dataset experiment also shows that our method has good generalization ability. Furthermore, our method is fast as our implementation runs at over 91 frames per second on a standard computer with a single GPU.

Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel algorithm to optimize the leaf weights in a Hough forest to assist global hand pose estimation with a single depth camera. Different from traditional Hough forest, we propose to learn the vote weights stored at the leaf nodes of a forest in a principled way to minimize average pose prediction error, so that ambiguous votes are largely suppressed during prediction fusion. Experiments show that the proposed method largely improves pose estimation accuracy with optimized leaf weights on both synthesis and real datasets and performs favorably compared to state-of-the-art convolutional neural network-based methods. On real-world depth videos, the proposed method demonstrates improved robustness compared to several other recent hand tracking systems from both industry and academy. Moreover, we utilize the proposed method to build virtual/augmented reality applications to allow users to manipulate and examine virtual objects with bare hands.

Articulated hand pose estimation is one of core technologies in human-computer interaction. Despite the recent progress, most existing methods still cannot achieve satisfactory performance, partly due to the difficulty of the embedded high-dimensional nonlinear regression problem. Most existing data-driven methods directly regress 3D hand pose from 2D depth image, which cannot fully utilize the depth information. In this paper, we propose a novel multi-view convolutional neural network (CNN)-based approach for 3D hand pose estimation. To better exploit 3D information in the depth image, we project the point cloud generated from the query depth image onto multiple views of two projection settings and integrate them for more robust estimation. Multi-view CNNs are trained to learn the mapping from projected images to heat-maps, which reflect probability distributions of joints on each view. These multi-view heat-maps are then fused to estimate the optimal 3D hand pose with learned pose priors, and the unreliable information in multi-view heat-maps is suppressed using a view selection method. Experimental results show that the proposed method is superior to the state-of-the-art methods on two challenging data sets. Furthermore, a cross-data set experiment also validates that our proposed approach has good generalization ability.

We propose a simple, yet effective approach for real-time hand pose estimation from single depth images using three-dimensional Convolutional Neural Networks (3D CNNs). Image based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information. Our proposed 3D CNN taking a 3D volumetric representation of the hand depth image as input can capture the 3D spatial structure of the input and accurately regress full 3D hand pose in a single pass. In order to make the 3D CNN robust to variations in hand sizes and global orientations, we perform 3D data augmentation on the training data. Experiments show that our proposed 3D CNN based approach outperforms state-of-the-art methods on two challenging hand pose datasets, and is very efficient as our implementation runs at over 215 fps on a standard computer with a single GPU.

Articulated hand pose estimation plays an important role in human-computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high-dimensional and non-linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane. These multi-view heat-maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment also demonstrates the good generalization ability of the proposed method.

This paper presents an efficient data-driven approach to track fingertip and detect finger tapping for virtual piano using an RGB-D camera. We collect 7200 depth images covering the most common finger articulation for playing piano, and train a random regression forest using depth context features of randomly sampled pixels in training images. In the online tracking stage, we firstly segment the hand from the plane in contact by fusing the information from both color and depth images. Then we use the trained random forest to estimate the 3D position of fingertips and wrist in each frame, and predict finger tapping based on the estimated fingertip motion. Finally, we build a kinematic chain and recover the articulation parameters for each finger. In contrast to the existing hand tracking algorithms that often require hands are in the air and cannot interact with physical objects, our method is designed for hand interaction with planar objects, which is desired for the virtual piano application. Using our prototype system, users can put their hands on a desk, move them sideways and then tap fingers on the desk, like playing a real piano. Preliminary results show that our method can recognize most of the beginner's piano-playing gestures in realtime for soothing rhythms.

Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility to recover both the articulated hand pose and its distance from the camera with a single RGB camera in egocentric view. We address this problem by modeling the distance as a hidden variable and use the Conditional Regression Forest to infer the pose and distance jointly. Especially, we find that the pose estimation accuracy can be further enhanced by incorporating the hand part semantics. The experimental results show that the proposed method achieves good performance on both a synthesized dataset and several real-world color image sequences that are captured in different environments. In addition, our system runs in real-time at more than 10fps.

Wearable devices such as Microsoft Hololens and Google glass are highly popular in recent years. As traditional input hardware is difficult to use on such platforms, vision-based hand pose tracking and gesture control techniques are more suitable alternatives. This demo shows the possibility to interact with 3D contents with bare hands on wearable devices by two Augmented Reality applications, including virtual teapot manipulation and fountain animation in hand. Technically, we use a head-mounted depth camera to capture the RGB-D images from egocentric view, and adopt the random forest to regress for the palm pose and classify the hand gesture simultaneously via a spatial-voting framework. The predicted pose and gesture are used to render the 3D virtual objects, which are overlaid onto the hand region in input RGB images with camera calibration parameters for seamless virtual and real scene synthesis.

The positions of the hand joints are important high-level features for hand-based human-computer interaction. We present a novel method to predict the 3-D joint positions from the depth images and the parsed hand parts obtained with a pretrained classifier. The hand parts are utilized as the additional cue to resolve the multimodal predictions produced by the previous regression-based method without increasing the computational cost significantly. In addition, we further enforce the hand motion constraints to fuse the per-pixel prediction results. The posterior distribution of the joints is formulated as a weighted product of experts model based on the individual pixel predictions, which is maximized via the expectation-maximization algorithm on a learned low-dimensional space of the hand joint parameters. The experimental results show the proposed method improves the prediction accuracy considerably compared with the rivals that also regress for the joint locations from the depth images. Especially, we show that the regressor learned on synthesized dataset also gives accurate prediction on real-world depth images by enforcing the hand part correlations despite their discrepancies.

Hand pose tracking and gesture recognition are useful for human-computer interaction, while a major problem is the lack of discriminative features for compact hand representation. We present a robust hand parsing scheme to extract a high-level description of the hand from the depth image. A novel distance-adaptive selection method is proposed to get more discriminative depth-context features. Besides, we propose a Superpixel-Markov Random Field (SMRF) parsing scheme to enforce the spatial smoothness and the label co-occurrence prior to remove the misclassified regions. Compared to pixel-level filtering, the SMRF scheme is more suitable to model the misclassified regions. By fusing the temporal constraints, its performance can be further improved. Overall, the proposed hand parsing scheme is accurate and efficient. The tests on synthesized dataset show it gives much higher accuracy for single-frame parsing and enhanced robustness for continuous sequence parsing compared to benchmarks. The tests on real-world depth images of the hand and human body show the robustness to complex hand configurations of our method and its generalization power to different kinds of articulated objects.

We present a vision-based approach for robust 3D fingertip and palm tracking on depth images using a single Kinect sensor. First the hand is segmented in the depth images by applying depth and morphological constraints. The palm is located by performing distance transform to the hand contour and tracked with a Kalman filter. The fingertips are detected by combining three depth-based features and tracked with a particle filter over successive frames. Quantitative results on synthetic depth sequences show the proposed scheme can track the fingertips quite accurately. Besides, its capabilities are further demonstrated through a real-life human-computer interaction application.