Text-Guided Texturing by Synchronized Multi-View Diffusion

This paper introduces a novel approach to synthesize texture to dress up a given 3D object, given a text prompt. Based on the pretrained text-to-image (T2I) diffusion model, existing methods usually employ a project-and-inpaint approach, in which a view of the given object is first generated and warped to another view for inpainting. But it tends to generate inconsistent texture due to the asynchronous diffusion of multiple views. We believe such asynchronous diffusion and insufficient information sharing among views are the root causes of the inconsistent artifact. In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency. To synchronize the diffusion, we share the denoised content among different views in each denoising step, specifically blending the latent content in the texture domain from views with overlap. Our method demonstrates superior performance in generating consistent, seamless, highly detailed textures, comparing to state-of-the-art methods

Improved Diffusion-based Image Colorization via Piggybacked Models

Image colorization has been attracting the research interests of the community for decades. However, existing methods still struggle to provide satisfactory colorized results given grayscale images due to a lack of human-like global understanding of colors. Recently, large-scale Text-to-Image (T2I) models have been exploited to transfer the semantic information from the text prompts to the image domain, where text provides a global control for semantic objects in the image. In this work, we introduce a colorization model piggybacking on the existing powerful T2I diffusion model. Our key idea is to exploit the color prior knowledge in the pre-trained T2I diffusion model for realistic and diverse colorization. A diffusion guider is designed to incorporate the pre-trained weights of the latent diffusion model to output a latent color prior that conforms to the visual semantics of the grayscale input. A lightness-aware VQVAE will then generate the colorized result with pixel-perfect alignment to the given grayscale image. Our model can also achieve conditional colorization with additional inputs (e.g. user hints and texts). Extensive experiments show that our method achieves state-of-the-art performance in terms of perceptual quality.Comment: project page: https://piggyback-color.github.io

Sketch2Manga: Shaded Manga Screening from Sketch with Diffusion Models

While manga is a popular entertainment form, creating manga is tedious, especially adding screentones to the created sketch, namely manga screening. Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty of generating high-quality shaded high-frequency screentones. The classic manga screening approaches generally require user input to provide screentone exemplars or a reference manga image. The recent deep learning models enables the automatic generation by learning from a large-scale dataset. However, the state-of-the-art models still fail to generate high-quality shaded screentones due to the lack of a tailored model and high-quality manga training data. In this paper, we propose a novel sketch-to-manga framework that first generates a color illustration from the sketch and then generates a screentoned manga based on the intensity guidance. Our method significantly outperforms existing methods in generating high-quality manga with shaded high-frequency screentones.Comment: 7 pages, 6 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Design of a tremor suppression orthosis based on structured fabrics

The number of people suffering from Parkinson’s Disease (PD) is increasing continuously, Parkinson’s disease often manifests symptoms of pathological tremors, which can seriously infect the patient’s daily life. There are no effective methods of curing Parkinson’s disease at the moment but to lessen the symptoms by suppressing the tremorous movements. Auxiliary equipment like orthosis and exoskeleton makes this possible, current designs of tremor suppression orthosis have problems of lack of portability and tremor suppression efficacy. To solve these problems, the dissertation proposes a lightweight, semi-active orthosis design based on structured fabrics. The orthosis takes advantage of a newly developed fabric material with tuneable properties, the material is soft in a relaxed state and can become stiff when the vacuum is applied. Two orthosis configurations were proposed, one with a single damper and the other with two dampers. The experiments show that the orthosis can restrict the flexibility of the wrist by 65% and 70% with single and double-damper configurations; the orthosis can also restrict the range of movements by 60% and 70% respectively in terms of the range of motion when the wrist is shaking.Master of Science (Smart Manufacturing

Realistic face animation generation from videos

3D face reconstruction and face alignment are two fundamental and highly related topics in computer vision. Recently, some works start to use deep learning models to estimate the 3DMM coefficients to reconstruct 3D face geometry. However, the performance is restricted due to the limitation of the predefined face templates. To address this problem, some end-toend methods, which can completely bypass the calculation of 3DMM coefficients, are proposed and attract much attention. In this report, we introduce and analyse three state-of-the-art methods in 3D face reconstruction and face alignment. Some potential improvement on PRN are proposed to further enhance its accuracy and speed

Construction of a CXC Chemokine-Based Prediction Model for the Prognosis of Colon Cancer

Colon cancer is the third most common cancer, with a high incidence and mortality. Construction of a specific and sensitive prediction model for prognosis is urgently needed. In this study, profiles of patients with colon cancer with clinical and gene expression data were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). CXC chemokines in patients with colon cancer were investigated by differential expression gene analysis, overall survival analysis, receiver operating characteristic analysis, gene set enrichment analysis (GSEA), and weighted gene coexpression network analysis. CXCL1, CXCL2, CXCL3, and CXCL11 were upregulated in patients with colon cancer and significantly correlated with prognosis. The area under curve (AUC) of the multigene forecast model of CXCL1, CXCL11, CXCL2, and CXCL3 was 0.705 in the GSE41258 dataset and 0.624 in TCGA. The prediction model was constructed using the risk score of the multigene model and three clinicopathological risk factors and exhibited 92.6% and 91.8% accuracy in predicting 3-year and 5-year overall survival of patients with colon cancer, respectively. In addition, by GSEA, expression of CXCL1, CXCL11, CXCL2, and CXCL3 was correlated with several signaling pathways, including NOD-like receptor, oxidative phosphorylation, mTORC1, interferon-gamma response, and IL6/JAK/STAT3 pathways. Patients with colon cancer will benefit from this prediction model for prognosis, and this will pave the way to improve the survival rate and optimize treatment for colon cancer