Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur

Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage

Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video

Synthesizing realistic videos according to a given speech is still an open challenge. Previous works have been plagued by issues such as inaccurate lip shape generation and poor image quality. The key reason is that only motions and appearances on limited facial areas (e.g., lip area) are mainly driven by the input speech. Therefore, directly learning a mapping function from speech to the entire head image is prone to ambiguity, particularly when using a short video for training. We thus propose a decomposition-synthesis-composition framework named Speech to Lip (Speech2Lip) that disentangles speech-sensitive and speech-insensitive motion/appearance to facilitate effective learning from limited training data, resulting in the generation of natural-looking videos. First, given a fixed head pose (i.e., canonical space), we present a speech-driven implicit model for lip image generation which concentrates on learning speech-sensitive motion and appearance. Next, to model the major speech-insensitive motion (i.e., head movement), we introduce a geometry-aware mutual explicit mapping (GAMEM) module that establishes geometric mappings between different head poses. This allows us to paste generated lip images at the canonical space onto head images with arbitrary poses and synthesize talking videos with natural head movements. In addition, a Blend-Net and a contrastive sync loss are introduced to enhance the overall synthesis performance. Quantitative and qualitative results on three benchmarks demonstrate that our model can be trained by a video of just a few minutes in length and achieve state-of-the-art performance in both visual quality and speech-visual synchronization. Code: https://github.com/CVMI-Lab/Speech2Lip

FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion

Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate the depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage frameworks are insufficient, limiting the performance of these approaches. To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparse-to-coarse stage and a coarse-to-fine stage. First, a coarse dense depth map is obtained by a simple CNN framework. Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with coarse depth map and color image as input. Specially, in the coarse-to-fine stage, a channel shuffle extraction operation is utilized to extract more representative features from color image and coarse depth map, and an energy based fusion operation is exploited to effectively fuse these features obtained by channel shuffle operation, thus leading to more accurate and refined depth maps. We achieve SoTA performance in RMSE on KITTI benchmark. Extensive experiments on other datasets future demonstrate the superiority of our approach over current state-of-the-art depth completion approaches

HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation

Self-supervised learning shows great potential in monocular depth estimation, using image sequences as the only source of supervision. Although people try to use the high-resolution image for depth estimation, the accuracy of prediction has not been significantly improved. In this work, we find the core reason comes from the inaccurate depth estimation in large gradient regions, making the bilinear interpolation error gradually disappear as the resolution increases. To obtain more accurate depth estimation in large gradient regions, it is necessary to obtain high-resolution features with spatial and semantic information. Therefore, we present an improved DepthNet, HR-Depth, with two effective strategies: (1) re-design the skip-connection in DepthNet to get better high-resolution features and (2) propose feature fusion Squeeze-and-Excitation(fSE) module to fuse feature more efficiently. Using Resnet-18 as the encoder, HR-Depth surpasses all previous state-of-the-art(SoTA) methods with the least parameters at both high and low resolution. Moreover, previous SoTA methods are based on fairly complex and deep networks with a mass of parameters which limits their real applications. Thus we also construct a lightweight network which uses MobileNetV3 as encoder. Experiments show that the lightweight network can perform on par with many large models like Monodepth2 at high-resolution with only20%parameters. All codes and models will be available at https://github.com/shawLyu/HR-Depth

Identification of Quorum Sensing Signal Molecule of Lactobacillus delbrueckii subsp. <i>bulgaricus</i>

Many bacteria in nature use quorum sensing (QS) to regulate gene expression. The quorum sensing system plays critical roles in the adaptation of bacteria to the surrounding environment. Previous studies have shown that during high-density fermentation, the autolysis of lactic acid bacteria was regulated by the QS system, and the two-component system (TCS, LBUL_RS00115/LBUL_RS00110) is involved in the autolysis of Lactobacillus delbrueckii subsp. <i>bulgaricus</i>. However, the QS signal molecule, which regulates this pathway, has not been identified. In this study, we compared the genome of Lactobacillus bulgaricus ATCC BAA-365 with the locus of seven lactobacillus QS systems; the position of the QS signal molecule of <i>Lactobacillus bulgaricus</i> ATCC BAA-365 was predicted by bioinformatics tool. Its function was identified by in vitro experiments. Construction of TCS mutant by gene knockout of LBUL_RS00115 confirmed that the signal molecule regulates the density of the flora by the TCS (LBUL_RS00115/LBUL_RS00110). This study indicated that quorum quenching and inhibition based on the signal molecule might serve as an approach to reduce the rate of autolysis of LAB and increase the number of live bacteria in fermentation

Data_Sheet_1_Antioxidant properties of water-soluble polysaccharides prepared by co-culture fermentation of straw and shrimp shell.PDF

Herein, we present a method for producing water-soluble polysaccharides (WSPs) by co-culture fermentation of straw and shrimp shells. The chitin-degrading strain was isolated and genotypically identified as the non-pathogen Photobacterium sp. LYM-1 in this study. Photobacterium sp. LYM-1 and Aureobasidium pullulans 2012 could coexist without antagonism. WSPs concentrations were higher in co-culture fermentations of Photobacterium sp. LYM-1 and A. pullulans 2012 (PsL/AP-WSPs) compared to monocultures (PsL-WSPs and AP-WSPs). FTIR was used to examine the polysaccharide properties of three WSP fractions. The monosaccharide compositions of three WSPs fractions were primarily composed of mannose, ribose, glucosamine, glucose, galactose, and arabinose with varying molecular weights and molar ratios according to HPLC analysis. PsL/AP-WSPs showed better scavenging effects on DPPH, ABTS, and OH free radicals, demonstrating the application potential of PsL/AP-WSPs from straw and shrimp shells. The maximum yield obtained under optimum conditions (fermentation time of 6 days, temperature of 31°C, inoculum concentration of 10% (w/v), and inoculum composition of 2:1) was 5.88 ± 0.40 mg/mL, based on the PsL/AP-WSPs production optimization by orthogonal design. The results suggest that an environmentally friendly approach for WSPs production from agro-food wastes straw and shrimp shells was developed.</p

Comparison of twelve human milk oligosaccharides in mature milk from different areas in China in the Chinese Human Milk Project (CHMP) study

Human milk oligosaccharides (HMOs) act as a vital role in the development of infant's gut microbiome and immune function. This study aimed to measure 12 oligosaccharides in milk from Chinese donors (n = 203), and evaluated the influences of multiple factors on the HMOs profiles. The results indicated that concentrations of 6′-sialyllactose were the highest among 12 oligosaccharides (2.31 ± 0.81 g/L). HMOs concentrations varied depending on geographical location. Latitude was observed to be related to concentrations of Lacto-N-neohexaose, lacto-N-fucopentaose III, 3′-sialyllactose (r = -0.67, r = +0.63 and r = +0.50, respectively). Environmental factors like seasons correlated with lacto-N-difucohexaose Ⅱ, Lacto-N-neohexaose and 2′-fucosyllactose (r = -0.47, r = -0.4, r = -0.35, respectively). Several HMOs concentrations were correlated with maternal diet. As a consequence, the HMOs profiles measured were influenced by geographical, environmental, maternal anthropometric as well as dietary factors