MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR

Based on powerful text-to-image diffusion models, text-to-3D generation has made significant progress in generating compelling geometry and appearance. However, existing methods still struggle to recover high-fidelity object materials, either only considering Lambertian reflectance, or failing to disentangle BRDF materials from the environment lights. In this work, we propose Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR (\textbf{MATLABER}) that leverages a novel latent BRDF auto-encoder for material generation. We train this auto-encoder with large-scale real-world BRDF collections and ensure the smoothness of its latent space, which implicitly acts as a natural distribution of materials. During appearance modeling in text-to-3D generation, the latent BRDF embeddings, rather than BRDF parameters, are predicted via a material network. Through exhaustive experiments, our approach demonstrates the superiority over existing ones in generating realistic and coherent object materials. Moreover, high-quality materials naturally enable multiple downstream tasks such as relighting and material editing. Code and model will be publicly available at \url{https://sheldontsui.github.io/projects/Matlaber}

DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.Comment: Accepted by CVPR 202

Comprehensive Analysis of Peripheral Exosomal circRNAs in Large Artery Atherosclerotic Stroke

Exosomes are crucial vehicles in intercellular communication. Circular RNAs (circRNAs), novel endogenous noncoding RNAs, play diverse roles in ischemic stroke. Recently, the abundance and stability of circRNAs in exosomes have been identified. However, a comprehensive analysis of exosomal circRNAs in large artery atherosclerotic (LAA) stroke has not yet been reported. We performed RNA sequencing (RNA-Seq) to comprehensively identify differentially expressed (DE) exosomal circRNAs in five paired LAA and normal controls. Further, quantitative real-time PCR (qRT-PCR) was used to verify the RNA-Seq results in a cohort of stroke patients (32 versus 32). RNA-Seq identified a total of 462 circRNAs in peripheral exosomes; there were 25 DE circRNAs among them. Additionally, circRNA competing endogenous RNA (ceRNA) network and translatable analysis revealed the potential functions of the exosomal circRNAs in LAA progression. Two ceRNA pathways involving 5 circRNAs, 2 miRNAs, and 3 mRNAs were confirmed by qRT-PCR. In the validation cohort, receiver operating characteristic (ROC) curve analysis identified two circRNAs as possible novel biomarkers, and a logistic model combining two and four circRNAs increased the area under the curve compared with the individual circRNAs. Here, we show for the first time the comprehensive expression of exosomal circRNAs, which displayed the potential diagnostic and biological function in LAA stroke

Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction

Neural surface reconstruction aims to reconstruct accurate 3D surfaces based on multi-view images. Previous methods based on neural volume rendering mostly train a fully implicit model with MLPs, which typically require hours of training for a single scene. Recent efforts explore the explicit volumetric representation to accelerate the optimization via memorizing significant information with learnable voxel grids. However, existing voxel-based methods often struggle in reconstructing fine-grained geometry, even when combined with an SDF-based volume rendering scheme. We reveal that this is because 1) the voxel grids tend to break the color-geometry dependency that facilitates fine-geometry learning, and 2) the under-constrained voxel grids lack spatial coherence and are vulnerable to local minima. In this work, we present Voxurf, a voxel-based surface reconstruction approach that is both efficient and accurate. Voxurf addresses the aforementioned issues via several key designs, including 1) a two-stage training procedure that attains a coherent coarse shape and recovers fine details successively, 2) a dual color network that maintains color-geometry dependency, and 3) a hierarchical geometry feature to encourage information propagation across voxels. Extensive experiments show that Voxurf achieves high efficiency and high quality at the same time. On the DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x training speedup compared to previous fully implicit methods

HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks

Portrait stylization is a long-standing task enabling extensive applications. Although 2D-based methods have made great progress in recent years, real-world applications such as metaverse and games often demand 3D content. On the other hand, the requirement of 3D data, which is costly to acquire, significantly impedes the development of 3D portrait stylization methods. In this paper, inspired by the success of 3D-aware GANs that bridge 2D and 3D domains with 3D fields as the intermediate representation for rendering 2D images, we propose a novel method, dubbed HyperStyle3D, based on 3D-aware GANs for 3D portrait stylization. At the core of our method is a hyper-network learned to manipulate the parameters of the generator in a single forward pass. It not only offers a strong capacity to handle multiple styles with a single model, but also enables flexible fine-grained stylization that affects only texture, shape, or local part of the portrait. While the use of 3D-aware GANs bypasses the requirement of 3D data, we further alleviate the necessity of style images with the CLIP model being the stylization guidance. We conduct an extensive set of experiments across the style, attribute, and shape, and meanwhile, measure the 3D consistency. These experiments demonstrate the superior capability of our HyperStyle3D model in rendering 3D-consistent images in diverse styles, deforming the face shape, and editing various attributes

Protein Kinase Cδ Suppresses Autophagy to Induce Kidney Cell Apoptosis in Cisplatin Nephrotoxicity

Nephrotoxicity is a major adverse effect in cisplatin chemotherapy, and renoprotective approaches are unavailable. Recent work unveiled a critical role of protein kinase Cδ (PKCδ) in cisplatin nephrotoxicity and further demonstrated that inhibition of PKCδ not only protects kidneys but enhances the chemotherapeutic effect of cisplatin in tumors; however, the underlying mechanisms remain elusive. Here, we show that cisplatin induced rapid activation of autophagy in cultured kidney tubular cells and in the kidneys of injected mice. Cisplatin also induced the phosphorylation of mammalian target of rapamycin (mTOR), p70S6 kinase downstream of mTOR, and serine/threonine-protein kinase ULK1, a component of the autophagy initiating complex. In vitro, pharmacologic inhibition of mTOR, directly or through inhibition of AKT, enhanced autophagy after cisplatin treatment. Notably, in both cells and kidneys, blockade of PKCδ suppressed the cisplatin-induced phosphorylation of AKT, mTOR, p70S6 kinase, and ULK1 resulting in upregulation of autophagy. Furthermore, constitutively active and inactive forms of PKCδ respectively enhanced and suppressed cisplatin-induced apoptosis in cultured cells. In mechanistic studies, we showed coimmunoprecipitation of PKCδ and AKT from lysates of cisplatin-treated cells and direct phosphorylation of AKT at serine-473 by PKCδin vitro Finally, administration of the PKCδ inhibitor rottlerin with cisplatin protected against cisplatin nephrotoxicity in wild-type mice, but not in renal autophagy-deficient mice. Together, these results reveal a pathway consisting of PKCδ, AKT, mTOR, and ULK1 that inhibits autophagy in cisplatin nephrotoxicity. PKCδ mediates cisplatin nephrotoxicity at least in part by suppressing autophagy, and accordingly, PKCδ inhibition protects kidneys by upregulating autophagy

Central role of microglia in sepsis-associated encephalopathy: From mechanism to therapy

Sepsis-associated encephalopathy (SAE) is a cognitive impairment associated with sepsis that occurs in the absence of direct infection in the central nervous system or structural brain damage. Microglia are thought to be macrophages of the central nervous system, devouring bits of neuronal cells and dead cells in the brain. They are activated in various ways, and microglia-mediated neuroinflammation is characteristic of central nervous system diseases, including SAE. Here, we systematically described the pathogenesis of SAE and demonstrated that microglia are closely related to the occurrence and development of SAE. Furthermore, we comprehensively discussed the function and phenotype of microglia and summarized their activation mechanism and role in SAE pathogenesis. Finally, this review summarizes recent studies on treating cognitive impairment in SAE by blocking microglial activation and toxic factors produced after activation. We suggest that targeting microglial activation may be a putative treatment for SAE