Multi-Modal Face Stylization with a Generative Prior

In this work, we introduce a new approach for artistic face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality stylized faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder-decoder architecture. Specifically, we use the mid-resolution and high-resolution layers of StyleGAN as the decoder to generate high-quality faces, while aligning its low-resolution layer with the encoder to extract and preserve input facial details. We also introduce a two-stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine-tuned with artistic data for stylized face generation. To enable the fine-tuned model to be applied in zero-shot and one-shot stylization tasks, we train an additional mapping network from the large-scale Contrastive-Language-Image-Pre-training (CLIP) space to a latent $w+$ space of fine-tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior face stylization performance in both one-shot and zero-shot stylization tasks, outperforming state-of-the-art methods by a large margin

Role Engine Implementation for a Continuous and Collaborative Multi-Robot System

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multi-robot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian Process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment's skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach's feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.Comment: 10 pages, 18 figures, summited in IEEE Transactions on Systems, Man and Cybernetics(T-SMC

Towards Practical Capture of High-Fidelity Relightable Avatars

In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, TRAvatar works in a more practical and efficient setting. Specifically, TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions, enabling realistic relighting and real-time animation for avatars in diverse scenes. Additionally, TRAvatar allows for tracking-free avatar capture and obviates the need for accurate surface tracking under varying illumination conditions. Our contributions are two-fold: First, we propose a novel network architecture that explicitly builds on and ensures the satisfaction of the linear nature of lighting. Trained on simple group light captures, TRAvatar can predict the appearance in real-time with a single forward pass, achieving high-quality relighting effects under illuminations of arbitrary environment maps. Second, we jointly optimize the facial geometry and relightable appearance from scratch based on image sequences, where the tracking is implicitly learned. This tracking-free approach brings robustness for establishing temporal correspondences between frames under different lighting conditions. Extensive qualitative and quantitative experiments demonstrate that our framework achieves superior performance for photorealistic avatar animation and relighting.Comment: Accepted to SIGGRAPH Asia 2023 (Conference); Project page: https://travatar-paper.github.io

m6A-related lncRNAs predict prognosis and indicate cell cycle in gastric cancer

Background: N6-methyladenosine (m6A) modification is a common epigenetic methylation modification of RNA, which plays an important role in gastric carcinogenesis and progression by regulating long non-coding RNA (lncRNA). This study is aimed to investigate the potential prognostic signatures of m6A -related lncRNAs in STAD.Methods: The m6A-related lncRNAs with the most significant impact on gastric cancer prognosis in the TCGA database were identified by bioinformatics and machine learning methods. The m6A-related lncRNA prognostic model (m6A-LPS) and nomogram was constructed by Cox regression analysis with the minimum absolute contraction and selection operator (LASSO) algorithm. The functional enrichment analysis of m6A-related lncRNAs was also investigated. The miRTarBase, miRDB and TargetScan databases were utilized to establish a prognosis-related network of competing endogenous RNA (ceRNA) by bioinformatics methods. The correlation of AL391152.1 expressions and cell cycle were experimentally testified by qRT-PCR and flow cytometry.Results: In total, 697 lncRNAs that were identified as m6A-related lncRNAs in GC samples. The survival analysis showed that 18 lncRNAs demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of GC patients. Cox regression analysis and ROC curve indicated that this lncRNA prediction model was an independent risk factor for survival rates. Functional enrichment analysis and ceRNA network revealed that the nomogram was notably associated with cell cycle. qRT-PCR and flow cytometry revealed that downregulation of GC m6A-related lncRNA AL391152.1 could decrease cyclins expression in SGC7901 cells.Conclusion: A m6A-related lncRNAs prognostic model was established in this study, which can be applied to predict prognosis and cell cycle in gastric cancer

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals