BodyNet: Volumetric Inference of 3D Human Body Shapes

Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. BodyNet is an end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them results in performance improvement as demonstrated by our experiments. To evaluate the method, we fit the SMPL model to our network output and show state-of-the-art results on the SURREAL and Unite the People datasets, outperforming recent approaches. Besides achieving state-of-the-art performance, our method also enables volumetric body-part segmentation.Comment: Appears in: European Conference on Computer Vision 2018 (ECCV 2018). 27 page

Características e controle da podridão "olho de boi" nas maçãs do sul do Brasil.

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Developing effective practice learning for tomorrow's social workers

This paper considers some of the changes in social work education in the UK, particularly focusing on practice learning in England. The changes and developments are briefly identified and examined in the context of what we know about practice learning. The paper presents some findings from a small scale qualitative study of key stakeholders involved in practice learning and education in social work and their perceptions of these anticipated changes, which are revisited at implementation. The implications for practice learning are discussed

Learning 3D Human Pose from Structure and Motion

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose

CONSTRUCTAL DESIGN OF FINS IN COOLED CAVITIES BY NON-NEWTONIAN FLUIDS

The present work investigates the Construtal Design of fins inserted in cavities submitted to mixed convection by non-Newtonian fluids. The objective is to obtain the optimum aspect ratio for the fin considering different flow conditions and variations in the rheological parameters of the fluid. The phenomena of flow and heat transfer are modeled by mass balance, momentum and energy equations, and by the generalized Newtonian liquid constitutive equation. The viscosity is modeled as that of a pseudoplastic fluid, using the Carreau function. The optimization problem consists in maximizing heat transfer from the fin using the average Nusselt number. The investigated project variable is the aspect ratio between the edges of the rectangular plane fin profile. The restrictions are the volume of the cavity and the fin. The results are obtained numerically using a finite volume code and a two-dimensional geometry, through exhaustive searching. The results show that the fin geometry influences the maximum Nusselt number mainly for the cases with high Reynolds and Rayleigh numbers, such as was shown in previous studies. The results show that the fin geometry influences the maximum Nusselt number mainly for the cases with high Reynolds and Rayleigh numbers, as was shown in previous studies. It was also found that the Nusselt number increases as the increase in flow intensity, represented by the parameter p, and that the result of the maximum Nusselt number does not change monotonically with the non-Newtonian dimensionless viscosity and with the flow index, showing that the pseudoplasticity of the fluid implies optimal configurations very different from those predicted for Newtonian fluids

NASA: Neural Articulated Shape Approximation

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), an alternative framework that enables efficient representation of articulated deformable objects using neural indicator functions that are conditioned on pose. Occupancy testing using NASA is straightforward, circumventing the complexity of meshes and the issue of water-tightness. We demonstrate the effectiveness of NASA for 3D tracking applications, and discuss other potential extensions.Comment: ECCV 202

Mitochondria dysfunction is associated with long-term cognitive impairment in an animal sepsis mode

Background: Several different mechanisms have been proposed to explain long-term cognitive impairment in sepsis survivors. The role of persisting mitochondrial dysfunction is not known. We thus sought to determine whether stimulation of mitochondrial dynamics improves mitochondrial function and long-term cognitive impairment in an experimental model of sepsis. Methods: Sepsis was induced in adult Wistar rats by cecal ligation and perforation (CLP). Animals received intracerebroventricular injections of either rosiglitazone (biogenesis activator), rilmenidine, rapamycin (autophagy activators), or n-saline (sham control) once a day on days 7–9 after the septic insult. Cognitive impairment was assessed by inhibitory avoidance and object recognition tests. Animals were killed 24 h, 3 and 10 days after sepsis with the hippocampus and prefrontal cortex removed to determine mitochondrial function. Results: Sepsis was associated with both acute (24 h) and late (10 days) brain mitochondrial dysfunction. Markers of mitochondrial biogenesis, autophagy and mitophagy were not up-regulated during these time points. Activation of biogenesis (rosiglitazone) or autophagy (rapamycin and rilmenidine) improved brain ATP levels and ex vivo oxygen consumption and the long-term cognitive impairment observed in sepsis survivors. Conclusion: Long-term impairment of brain function is temporally related to mitochondrial dysfunction. Activators of autophagy and mitochondrial biogenesis could rescue animals from cognitive impairment

Evaluating the quality of social work supervision in UK children's services: comparing self-report and independent observations

Understanding how different forms of supervision support good social work practice and improve outcomes for people who use services is nearly impossible without reliable and valid evaluative measures. Yet the question of how best to evaluate the quality of supervision in different contexts is a complicated and as-yet-unsolved challenge. In this study, we observed 12 social work supervisors in a simulated supervision session offering support and guidance to an actor playing the part of an inexperienced social worker facing a casework-related crisis. A team of researchers analyzed these sessions using a customized skills-based coding framework. In addition, 19 social workers completed a questionnaire about their supervision experiences as provided by the same 12 supervisors. According to the coding framework, the supervisors demonstrated relatively modest skill levels, and we found low correlations among different skills. In contrast, according to the questionnaire data, supervisors had relatively high skill levels, and we found high correlations among different skills. The findings imply that although self-report remains the simplest way to evaluate supervision quality, other approaches are possible and may provide a different perspective. However, developing a reliable independent measure of supervision quality remains a noteworthy challenge

Monocular Expressive Body Regression through Body-Driven Attention

To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .Comment: Accepted in ECCV'20. Project page: http://expose.is.tue.mpg.d