Computational Learning for Hand Pose Estimation

Rapid advances in human–computer interaction interfaces have been promising a realistic environment for gaming and entertainment in the last few years. However, the use of traditional input devices such as trackballs, keyboards, or joysticks has been a bottleneck for natural interactions between a human and computer as two points of freedom of these devices cannot suitably emulate the interactions in a three-dimensional space. Consequently, a comprehensive hand tracking technology is expected as a smart and intuitive option to these input tools to enhance virtual and augmented reality experiences. In addition, the recent emergence of low-cost depth sensing cameras has led to their broad use of RGB-D data in computer vision, raising expectations of a full 3D interpretation of hand movements for human–computer interaction interfaces. Although the use of hand gestures or hand postures has become essential for a wide range of applications in computer games and augmented/virtual reality, 3D hand pose estimation is still an open and challenging problem because of the following reasons: (i) the hand pose exists in a high-dimensional space because each finger and the palm is associated with several degrees of freedom, (ii) the fingers exhibit self-similarity and often occlude to each other, (iii) global 3D rotations make pose estimation more difficult, and (iv) hands only exist in few pixels in images and the noise in acquired data coupled with fast finger movement confounds continuous hand tracking. The success of hand tracking would naturally depend on synthesizing our knowledge of the hand (i.e., geometric shape, constraints on pose configurations) and latent features about hand poses from the RGB-D data stream (i.e., region of interest, key feature points like finger tips and joints, and temporal continuity). In this thesis, we propose novel methods to leverage the paradigm of analysis by synthesis and create a prediction model using a population of realistic 3D hand poses. The overall goal of this work is to design a concrete framework so the computers can learn and understand about perceptual attributes of human hands (i.e., self-occlusions or self-similarities of the fingers) and to develop a pragmatic solution to the real-time hand pose estimation problem implementable on a standard computer. This thesis can be broadly divided into four parts: learning hand (i) from recommendiations of similar hand poses, (ii) from low-dimensional visual representations, (iii) by hallucinating geometric representations, and (iv) from a manipulating object. Each research work covers our algorithmic contributions to solve the 3D hand pose estimation problem. Additionally, the research work in the appendix proposes a pragmatic technique for applying our ideas to mobile devices with low computational power. Following a given structure, we first overview the most relevant works on depth sensor-based 3D hand pose estimation in the literature both with and without manipulating an object. Two different approaches prevalent for categorizing hand pose estimation, model-based methods and appearance-based methods, are discussed in detail. In this chapter, we also introduce some works relevant to deep learning and trials to achieve efficient compression of the network structure. Next, we describe a synthetic 3D hand model and its motion constraints for simulating realistic human hand movements. The section for the primary research work starts in the following chapter. We discuss our attempts to produce a better estimation model for 3D hand pose estimation by learning hand articulations from recommendations of similar poses. Specifically, the unknown pose parameters for input depth data are estimated by collaboratively learning the known parameters of all neighborhood poses. Subsequently, we discuss deep-learned, discriminative, and low-dimensional features and a hierarchical solution of the stated problem based on the matrix completion framework. This work is further extended by incorporating a function of geometric properties on the surface of the hand described by heat diffusion, which is robust to capture both the local geometry of the hand and global structural representations. The problem of the hands interactions with a physical object is also considered in the following chapter. The main insight is that the interacting object can be a source of constraint on hand poses. In this view, we employ pose dependency on the shape of the object to learn the discriminative features of the hand–object interaction, rather than losing hand information caused by partial or full object occlusions. Subsequently, we present a compressive learning technique in the appendix. Our approach is flexible, enabling us to add more layers and go deeper in the deep learning architecture while keeping the number of parameters the same. Finally, we conclude this thesis work by summarizing the presented approaches for hand pose estimation and then propose future directions to further achieve performance improvements through (i) realistically rendered synthetic hand images, (ii) incorporating RGB images as an input, (iii) hand perseonalization, (iv) use of unstructured point cloud, and (v) embedding sensing techniques

SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1cm position error and <5^\circ$ angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read

Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

We propose a simple and efficient method for exploiting synthetic images when training a Deep Network to predict a 3D pose from an image. The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome. However, synthetic images do not resemble real images exactly, and using them for training can result in suboptimal performance. It was recently shown that for exemplar-based approaches, it is possible to learn a mapping from the exemplar representations of real images to the exemplar representations of synthetic images. In this paper, we show that this approach is more general, and that a network can also be applied after the mapping to infer a 3D pose: At run time, given a real image of the target object, we first compute the features for the image, map them to the feature space of synthetic images, and finally use the resulting features as input to another network which predicts the 3D pose. Since this network can be trained very effectively by using synthetic images, it performs very well in practice, and inference is faster and more accurate than with an exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for 3D object pose estimation from color images, and the NYU dataset for 3D hand pose estimation from depth maps. We show that it allows us to outperform the state-of-the-art on both datasets.Comment: CVPR 201

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.Comment: HANDS 2017 Challenge Frame-based 3D Hand Pose Estimation Winner (ICCV 2017), Published at CVPR 201

Learning Human Pose Estimation Features with Convolutional Networks

This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced

GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB

We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a kinematic 3D hand model, such that it generalizes well to unseen data, is robust to occlusions and varying camera viewpoints, and leads to anatomically plausible as well as temporally smooth hand motions. For training our CNN we propose a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network. To be more specific, we use a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images. For training this translation network we combine an adversarial loss and a cycle-consistency loss with a geometric consistency loss in order to preserve geometric properties (such as hand pose) during translation. We demonstrate that our hand tracking system outperforms the current state-of-the-art on challenging RGB-only footage