LEARNABLE MASKS FOR POSE-GUIDED VIEW SYNTHESIS

Pose-guided human view synthesis uses a target pose to generate the appearance of a new view of a person. The input view and the target pose can be processed separately with UNet architectures that combine the results in a late fusion stage. UNet architectures link their encoder and decoder with skip connections that preserve the location of spatial features by injecting input information in the decoding process. However, direct skip connections may transfer irrelevant information to the decoder. We overcome this limitation with learnable masks for skip connections that encourage the decoder to use only relevant information from the encoder. We show that adding the proposed mask to UNet architectures improves the performance of view synthesis with only a slight increase in inference time

XingGAN for Person Image Generation

We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The proposed Xing generator consists of two generation branches that model the person's appearance and shape information, respectively. Moreover, we propose two novel blocks to effectively transfer and update the person's shape and appearance embeddings in a crossing way to mutually improve each other, which has not been considered by any other existing GAN-based image generation work. Extensive experiments on two challenging datasets, i.e., Market-1501 and DeepFashion, demonstrate that the proposed XingGAN advances the state-of-the-art performance both in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/XingGAN.Comment: Accepted to ECCV 2020, camera ready (16 pages) + supplementary (6 pages

Fluid challenges in intensive care: the FENICE study A global inception cohort study

Fluid challenges (FCs) are one of the most commonly used therapies in critically ill patients and represent the cornerstone of hemodynamic management in intensive care units. There are clear benefits and harms from fluid therapy. Limited data on the indication, type, amount and rate of an FC in critically ill patients exist in the literature. The primary aim was to evaluate how physicians conduct FCs in terms of type, volume, and rate of given fluid; the secondary aim was to evaluate variables used to trigger an FC and to compare the proportion of patients receiving further fluid administration based on the response to the FC.This was an observational study conducted in ICUs around the world. Each participating unit entered a maximum of 20 patients with one FC.2213 patients were enrolled and analyzed in the study. The median [interquartile range] amount of fluid given during an FC was 500 ml (500-1000). The median time was 24 min (40-60 min), and the median rate of FC was 1000 [500-1333] ml/h. The main indication for FC was hypotension in 1211 (59 %, CI 57-61 %). In 43 % (CI 41-45 %) of the cases no hemodynamic variable was used. Static markers of preload were used in 785 of 2213 cases (36 %, CI 34-37 %). Dynamic indices of preload responsiveness were used in 483 of 2213 cases (22 %, CI 20-24 %). No safety variable for the FC was used in 72 % (CI 70-74 %) of the cases. There was no statistically significant difference in the proportion of patients who received further fluids after the FC between those with a positive, with an uncertain or with a negatively judged response.The current practice and evaluation of FC in critically ill patients are highly variable. Prediction of fluid responsiveness is not used routinely, safety limits are rarely used, and information from previous failed FCs is not always taken into account

View-LSTM: Novel-View Video Synthesis Through View Decomposition

We tackle the problem of synthesizing a video of multiple moving people as seen from a novel view, given only an input video and depth information or human poses of the novel view as prior. This problem requires a model that learns to transform input features into target features while maintaining temporal consistency. To this end, we learn an invariant feature from the input video that is shared across all viewpoints of the same scene and a view-dependent feature obtained using the target priors. The proposed approach, View-LSTM, is a recurrent neural network structure that accounts for the temporal consistency and target feature approximation constraints. We validate View-LSTM by designing an end-to-end generator for novel-view video synthesis. Experiments on a large multi-view action recognition dataset validate the proposed model

Dynamic arterial elastance as a predictor of arterial pressure response to fluid administration: a validation study.

INTRODUCTION: Functional assessment of arterial load by dynamic arterial elastance (Eadyn), defined as the ratio between pulse pressure variation (PPV) and stroke volume variation (SVV), has recently been shown to predict the arterial pressure response to volume expansion (VE) in hypotensive, preload-dependent patients. However, because both SVV and PPV were obtained from pulse pressure analysis, a mathematical coupling factor could not be excluded. We therefore designed this study to confirm whether Eadyn, obtained from two independent signals, allows the prediction of arterial pressure response to VE in fluid-responsive patients. METHODS: We analyzed the response of arterial pressure to an intravenous infusion of 500 ml of normal saline in 53 mechanically ventilated patients with acute circulatory failure and preserved preload dependence. Eadyn was calculated as the simultaneous ratio between PPV (obtained from an arterial line) and SVV (obtained by esophageal Doppler imaging). A total of 80 fluid challenges were performed (median, 1.5 per patient; interquartile range, 1 to 2). Patients were classified according to the increase in mean arterial pressure (MAP) after fluid administration in pressure responders (≥ 10%) and non-responders. RESULTS: Thirty-three fluid challenges (41.2%) significantly increased MAP. At baseline, Eadyn was higher in pressure responders (1.04 ± 0.28 versus 0.60 ± 0.14; P < 0.0001). Preinfusion Eadyn was related to changes in MAP after fluid administration (R (2) = 0.60; P < 0.0001). At baseline, Eadyn predicted the arterial pressure increase to volume expansion (area under the receiver operating characteristic curve, 0.94; 95% confidence interval (CI): 0.86 to 0.98; P < 0.0001). A preinfusion Eadyn value ≥ 0.73 (gray zone: 0.72 to 0.88) discriminated pressure responder patients with a sensitivity of 90.9% (95% CI: 75.6 to 98.1%) and a specificity of 91.5% (95% CI: 79.6 to 97.6%). CONCLUSIONS: Functional assessment of arterial load by Eadyn, obtained from two independent signals, enabled the prediction of arterial pressure response to fluid administration in mechanically ventilated, preload-dependent patients with acute circulatory failure