ProbIBR: Fast Image-Based Rendering with Learned Probability-Guided Sampling.

We present a general, fast, and practical solution for interpolating novel views of diverse real-world scenes given a sparse set of nearby views. Existing generic novel view synthesis methods rely on time-consuming scene geometry pre-computation or redundant sampling of the entire space for neural volumetric rendering, limiting the overall efficiency. Instead, we incorporate learned MVS priors into the neural volume rendering pipeline while improving the rendering efficiency by reducing sampling points under the guidance of depth probability distributions.

Metadherin orchestrates PKA and PKM2 to activate β-catenin signaling in podocytes during proteinuric chronic kidney disease.

Podocyte damage is the major cause of glomerular injury and proteinuria in multiple chronic kidney diseases. Metadherin (MTDH) is involved in podocyte apoptosis and promotes renal tubular injury in mouse models of diabetic nephropathy and renal fibrosis; however, its role in podocyte injury and proteinuria needs further exploration. Here, we show that MTDH was induced in the glomerular podocytes of patients with proteinuric chronic kidney disease and correlated with proteinuria.

Renal Fibrosis Is Alleviated through Targeted Inhibition of IL-11-Induced Renal Tubular Epithelial-to-Mesenchymal Transition.

Renal fibrosis is a pathologic process that leads to irreversible renal failure without effective treatment. Epithelial-to-mesenchymal transition (EMT) plays a key role in this process. The current study found that aberrant expression of IL-11 is critically involved in tubular EMT.

HVTR++: Image and Pose Driven Human Avatars Using Hybrid Volumetric-Textural Rendering.

Recent neural rendering methods have made great progress in generating photorealistic human avatars. However, these methods are generally conditioned only on low-dimensional driving signals (e.g.

Robust and Accurate 3D Self-Portraits in Seconds.

In this paper, we propose an efficient method for robust and accurate 3D self-portraits using a single RGBD camera. Our method can generate detailed and realistic 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the subject.

PaMIR: Parametric Model-Conditioned Implicit Representation for Image-Based Human Reconstruction.

Modeling 3D humans accurately and robustly from a single image is very challenging, and the key for such an ill-posed problem is the 3D representation of the human models. To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function. In our PaMIR-based reconstruction framework, a novel deep neural network is proposed to regularize the free-form deep implicit function using the semantic features of the parametric model, which improves the generalization ability under the scenarios of challenging poses and various clothing topologies.

Serum exosomal microRNA-766-3p expression is associated with poor prognosis of esophageal squamous cell carcinoma.

The aim was to analyze the association between exosomal microRNA (miR)-766-3p expression levels in serum and the prognosis of esophageal squamous cell carcinoma (ESCC). The serum global exosomal miRNA expression of ESCC patients was measured by microRNA microarray. Quantitative real-time PCR was used to analyze the expression levels of candidate miRNAs in both serum and tissues from ESCC patients.

DoubleFusion: Real-Time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor.

We propose DoubleFusion, a new real-time system that combines volumetric non-rigid reconstruction with data-driven template fitting to simultaneously reconstruct detailed surface geometry, large non-rigid motion and the optimized human body shape from a single depth camera. One of the key contributions of this method is a double-layer representation consisting of a complete parametric body model inside, and a gradually fused detailed surface outside. A pre-defined node graph on the body parameterizes the non-rigid deformations near the body, and a free-form dynamically changing graph parameterizes the outer surface layer far from the body, which allows more general reconstruction.