Monocular tracking of the human arm in 3D: real-time implementation and experiments

We have developed a system capable of tracking a human arm in 3D and in real time. The system is based on a previously developed algorithm for 3D tracking which requires only a monocular view and no special markers on the body. In this paper we describe our real-time system and the insights gained from real-time experimentation

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.Comment: Accepted to SIGGRAPH 201

Exploiting temporal information for 3D pose estimation

In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately $12.2\%$ and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails

Fidelity metrics for virtual environment simulations based on spatial memory awareness states

This paper describes a methodology based on human judgments of memory awareness states for assessing the simulation fidelity of a virtual environment (VE) in relation to its real scene counterpart. To demonstrate the distinction between task performance-based approaches and additional human evaluation of cognitive awareness states, a photorealistic VE was created. Resulting scenes displayed on a headmounted display (HMD) with or without head tracking and desktop monitor were then compared to the real-world task situation they represented, investigating spatial memory after exposure. Participants described how they completed their spatial recollections by selecting one of four choices of awareness states after retrieval in an initial test and a retention test a week after exposure to the environment. These reflected the level of visual mental imagery involved during retrieval, the familiarity of the recollection and also included guesses, even if informed. Experimental results revealed variations in the distribution of participants’ awareness states across conditions while, in certain cases, task performance failed to reveal any. Experimental conditions that incorporated head tracking were not associated with visually induced recollections. Generally, simulation of task performance does not necessarily lead to simulation of the awareness states involved when completing a memory task. The general premise of this research focuses on how tasks are achieved, rather than only on what is achieved. The extent to which judgments of human memory recall, memory awareness states, and presence in the physical and VE are similar provides a fidelity metric of the simulation in question

Selective rendering for efficient ray traced stereoscopic images

Depth-related visual effects are a key feature of many virtual environments. In stereo-based systems, the depth effect can be produced by delivering frames of disparate image pairs, while in monocular environments, the viewer has to extract this depth information from a single image by examining details such as perspective and shadows. This paper investigates via a number of psychophysical experiments, whether we can reduce computational effort and still achieve perceptually high-quality rendering for stereo imagery. We examined selectively rendering the image pairs by exploiting the fusing capability and depth perception underlying human stereo vision. In ray-tracing-based global illumination systems, a higher image resolution introduces more computation to the rendering process since many more rays need to be traced. We first investigated whether we could utilise the human binocular fusing ability and significantly reduce the resolution of one of the image pairs and yet retain a high perceptual quality under stereo viewing condition. Secondly, we evaluated subjects' performance on a specific visual task that required accurate depth perception. We found that subjects required far fewer rendered depth cues in the stereo viewing environment to perform the task well. Avoiding rendering these detailed cues saved significant computational time. In fact it was possible to achieve a better task performance in the stereo viewing condition at a combined rendering time for the image pairs less than that required for the single monocular image. The outcome of this study suggests that we can produce more efficient stereo images for depth-related visual tasks by selective rendering and exploiting inherent features of human stereo vision

Direct interaction with large displays through monocular computer vision

Large displays are everywhere, and have been shown to provide higher productivity gain and user satisfaction compared to traditional desktop monitors. The computer mouse remains the most common input tool for users to interact with these larger displays. Much effort has been made on making this interaction more natural and more intuitive for the user. The use of computer vision for this purpose has been well researched as it provides freedom and mobility to the user and allows them to interact at a distance. Interaction that relies on monocular computer vision, however, has not been well researched, particularly when used for depth information recovery. This thesis aims to investigate the feasibility of using monocular computer vision to allow bare-hand interaction with large display systems from a distance. By taking into account the location of the user and the interaction area available, a dynamic virtual touchscreen can be estimated between the display and the user. In the process, theories and techniques that make interaction with computer display as easy as pointing to real world objects is explored. Studies were conducted to investigate the way human point at objects naturally with their hand and to examine the inadequacy in existing pointing systems. Models that underpin the pointing strategy used in many of the previous interactive systems were formalized. A proof-of-concept prototype is built and evaluated from various user studies. Results from this thesis suggested that it is possible to allow natural user interaction with large displays using low-cost monocular computer vision. Furthermore, models developed and lessons learnt in this research can assist designers to develop more accurate and natural interactive systems that make use of human’s natural pointing behaviours