Thank you very much for downloading the practice of everyday life. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this the practice of everyday life, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their desktop computer. the practice of everyday life is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the the practice of everyday life is universally compatible with any devices to read.

The Practice Of Everyday Life

A morphable model for the synthesis of 3D faces

Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.

/pdf/learning-from-synthetic-humans-20jud1j4qg.pdf

Learning from Synthetic Humans

Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task. This paper tackles this challenging and essentially underdetermined problem by regression on deep convolutional neural network (DCNN) features, combined with a post-processing refining step using conditional random fields (CRF). Our framework works at two levels, super-pixel level and pixel level. First, we design a DCNN model to learn the mapping from multi-scale image patches to depth or surface normal values at the super-pixel level. Second, the estimated super-pixel depth or surface normal is refined to the pixel level by exploiting various potentials on the depth or surface normal map, which includes a data term, a smoothness term among super-pixels and an auto-regression term characterizing the local structure of the estimation map. The inference problem can be efficiently solved because it admits a closed-form solution. Experiments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.

/pdf/depth-and-surface-normal-estimation-from-monocular-images-3gqdvcsuzt.pdf

Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs

Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.

Embodied hands: modeling and capturing hands and bodies together

Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective function; (2) a smooth-surface model that provides gradients for non-linear optimization; and (3) joint optimization over both the model pose and the correspondences between observed data points and the model surface. While each of these changes may actually increase the cost per fitting iteration, we find a compensating decrease in the number of iterations. Further, the wide basin of convergence means that fewer starting points are needed for successful model fitting. Our system runs in real-time on CPU only, which frees up the commonly over-burdened GPU for experience designers. The hand tracker is efficient enough to run on low-power devices such as tablets. We can track up to several meters from the camera to provide a large working volume for interaction, even using the noisy data from current-generation depth cameras. Quantitative assessments on standard datasets show that the new approach exceeds the state of the art in accuracy. Qualitative results take the form of live recordings of a range of interactive experiences enabled by this new approach.

/pdf/efficient-and-precise-interactive-hand-tracking-through-17u6zlizb5.pdf

Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences

We present findings from a year-long engagement with a street and its community. The work explores how the production and use of data is bound up with place, both in terms of physical and social geography. We detail three strands of the project. First, we consider how residents have sought to curate existing data about the street in the form of an archive with physical and digital components. Second, we report endeavours to capture data about the street's environment, especially of vehicle traffic. Third, we draw on the possibilities afforded by technologies for polling opinion. We reflect on how these engagements have: materialised distinctive relations between the community and their data; surfaced flows and contours of data, and spatial, temporal and social boundaries; and enacted a multiplicity of 'small worlds'. We consider how such a conceptualisation of data-in-place is relevant to the design of technology.

/pdf/data-in-place-thinking-through-the-relations-between-data-2n1f4ad85i.pdf

Data-in-Place: Thinking through the Relations Between Data and Community

We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute, metric depth in real-time. We demonstrate a variety of human-computer interaction and capture scenarios. Experiments show an accuracy that outperforms a conventional light fall-off baseline, and is comparable to high-quality consumer depth cameras, but with a dramatically reduced cost, power consumption, and form-factor.

/pdf/learning-to-be-a-depth-camera-for-close-range-human-capture-49iw15fdcr.pdf

Learning to be a depth camera for close-range human capture and interaction

We explore the design space of energy-neutral situated displays, which give physical presence to digital information. We investigate three central dimensions: energy sources, display technologies, and wireless communications. Based on the power implications from our analysis, we present a thin, wireless, photovoltaic-powered display that is quick and easy to deploy and capable of indefinite operation in indoor lighting conditions. The display uses a low-resolution e-paper architecture, which is 35 times more energy-efficient than smaller-sized high-resolution displays. We present a detailed analysis on power consumption, photovoltaic energy harvesting performance, and a detailed comparison to other display-driving architectures. Depending on the ambient lighting, the display can trigger an update every 1 -- 25 minutes and communicate to a PC or smartphone via Bluetooth Low-Energy.

/pdf/exploring-the-design-space-for-energy-harvesting-situated-3hxbh1w6h9.pdf

Exploring the Design Space for Energy-Harvesting Situated Displays

What does the abundance of data and proliferation of data-making methods mean for the ordinary person, the person on the street? And, what could they come to mean? In this paper, we present an overview of a year-long project to examine just such questions and complicate, in some ways, what it is to ask them. The project is a collective exercise in which we – a mixture of social scientists, designers and makers – and those living and working on one street in Cambridge (UK), Tenison Road, are working to think through how data might be materialised and come to matter. The project aims to better understand the specificities and contingencies that arise when data is produced and used in place. Mid-way through the project, we use this commentary to give some background to the work and detail one or two of the troubles we have encountered in putting locally relevant data to work. We also touch on a methodological standpoint we are working our way into and through, one that we hope complicates the separations between subject and object in datamaking and opens up possibilities for a generative refiguring of the manifold relations.

/pdf/data-and-life-on-the-street-4wxfrnhh9m.pdf

David Sweeney

Papers

Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences

Data-in-Place: Thinking through the Relations Between Data and Community

Learning to be a depth camera for close-range human capture and interaction

Exploring the Design Space for Energy-Harvesting Situated Displays

Data and life on the street