Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation

Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold structures of data. In particular, NTF suffers from rotational ambiguity, whose solutions with and without rotation transformations are equally in the sense of yielding the maximum likelihood. In this paper, we propose three Robust Manifold NTF algorithms to handle outliers by incorporating structural knowledge about the outliers. They first applies a half-quadratic optimization algorithm to transform the problem into a general weighted NTF where the weights are influenced by the outliers. Then, we introduce the correntropy induced metric, Huber function and Cauchy function for weights respectively, to handle the outliers. Finally, we introduce a manifold regularization to overcome the rotational ambiguity of NTF. We have compared the proposed method with a number of representative references covering major branches of NTF on a variety of real-world image databases. Experimental results illustrate the effectiveness of the proposed method under two evaluation metrics (accuracy and nmi)

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage. In this paper, we propose an efficient NeRF-based framework that enables real-time synthesizing of talking portraits and faster convergence by leveraging the recent success of grid-based NeRF. Our key insight is to decompose the inherently high-dimensional talking portrait representation into three low-dimensional feature grids. Specifically, a Decomposed Audio-spatial Encoding Module models the dynamic head with a 3D spatial grid and a 2D audio grid. The torso is handled with another 2D grid in a lightweight Pseudo-3D Deformable Module. Both modules focus on efficiency under the premise of good rendering quality. Extensive experiments demonstrate that our method can generate realistic and audio-lips synchronized talking portrait videos, while also being highly efficient compared to previous methods.Comment: Project page: https://me.kiui.moe/radnerf

MicroRNA-1224 Inhibits Tumor Metastasis in Intestinal-Type Gastric Cancer by Directly Targeting FAK

Intestinal-type gastric cancer (GC) of the Lauren classification system has specific epidemiological characteristics and carcinogenesis patterns. MicroRNAs (miRNAs) have prognostic significance, and some can be used as prognostic biomarkers in GC. In this study, we identified miR-1224 as a potential survival-related miRNA in intestinal-type GC patients by The Cancer Genome Atlas (TCGA) analysis. Using quantitative real-time PCR (qRT-PCR), we showed that the relative expression of miR-1224 was significantly decreased in intestinal-type GC tissues compared to matched adjacent normal mucosa tissues (p < 0.01). We found that high miR-1224 expression was associated with no lymph-node metastasis (p < 0.05) and good prognosis (p = 0.028) in 90 intestinal-type GC tissues. Transfection of intestinal-type GC cells with miR-1224 mimics showed that miR-1224 suppressed cell migration in vitro (wound healing assay and Transwell migration assay), whereas the transfection of cells with miR-1224 inhibitor promoted cell migration in vitro. miR-1224 also suppressed intestinal-type GC cell metastasis in a xenograft mouse model. Furthermore, bioinformatics, luciferase reporter, Western blotting, and immunohistochemistry (IHC) studies demonstrated that miR-1224 directly bound to the focal adhesion kinase (FAK) gene, and downregulated its expression, which decreased STAT3 and NF-κB signaling and subsequent the epithelial-to-mesenchymal transition (EMT). Repression of FAK is required for the miR-1224-mediated inhibition of cell migration in intestinal-type GC. The present study demonstrated that miR-1224 is downregulated in intestinal-type GC. miR-1224 inhibits the metastasis of intestinal-type GC by suppressing FAK-mediated activation of the STAT3 and NF-κB pathways, and subsequent EMT. miR-1224 could represent an important prognostic factor in intestinal-type GC