Video-driven Neural Physically-based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.Comment: For project page, see https://sites.google.com/view/npfa/ Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of databas

Relightable Neural Human Assets from Multi-view Gradient Illuminations

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2,000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github.io/RNHA.Comment: Project page: https://miaoing.github.io/RNH

Application of seaweed polysaccharide in bone tissue regeneration

Regeneration is a complex process influenced by many independent or combined factors, including inflammation, proliferation, and tissue remodeling. The ocean, the most extensive resource on Earth, is rich in Seaweed. With increasing research in recent years, researchers have discovered that seaweed polysaccharides have various pharmacological effects, including a particular efficacy in promoting bone tissue regeneration. However, the application of this material in the field of bone tissue engineering is very limited. However, there are few studies on the polysaccharide at home and abroad, and little is known about its potential application value in bone repair. In addition, the bioavailability of the seaweed polysaccharide is also low, and there are still many problems to be solved. For example, the ease of solubility of fucoidan in water is a key issue that restricts its practical application. In this review, we summarize the applications and mechanisms of seaweed polysaccharides in bone healing. We also propose to combine seaweed polysaccharides with novel technologies through different types of preparations, hydrogels, scaffolds, and 3D printing to improve their use in tissue healing and regeneration

SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator

Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner skeletal structures and appearance. In this paper, we present SCULPTOR, 3D face creations with Skeleton Consistency Using a Learned Parametric facial generaTOR, aiming to facilitate easy creation of both anatomically correct and visually convincing face models via a hybrid parametric-physical representation. At the core of SCULPTOR is LUCY, the first large-scale shape-skeleton face dataset in collaboration with plastic surgeons. Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, critical for evaluating surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41 female) where each subject has two CT scans taken pre- and post-orthognathic operations. Based on our LUCY dataset, we learn a novel skeleton consistent parametric facial generator, SCULPTOR, which can create the unique and nuanced facial features that help define a character and at the same time maintain physiological soundness. Our SCULPTOR jointly models the skull, face geometry and face appearance under a unified data-driven framework, by separating the depiction of a 3D face into shape blend shape, pose blend shape and facial expression blend shape. SCULPTOR preserves both anatomic correctness and visual realism in facial generation tasks compared with existing methods. Finally, we showcase the robustness and effectiveness of SCULPTOR in various fancy applications unseen before.Comment: 16 page, 13 fig

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

An Agent-Based Model of a Pricing Process with Power Law, Volatility Clustering, and Jumps

In this paper, we propose a new model of security price dynamics in order to explain the stylized facts of the pricing process such as power law distribution, volatility clustering, jumps, and structural changes. We assume that there are two types of agents in the financial market: speculators and fundamental investors. Speculators use past prices to predict future prices and only buy assets whose prices are expected to rise. Fundamental investors attach a certain value to each asset and buy when the asset is undervalued by the market. When the expectations of agents are exogenously driven, that is, entirely shaped by exogenous news, then they can be modeled as following a random walk. We assume that the information related to the two types of agents in the model will arrive randomly with a certain probability distribution and change the viewpoint of the agents according to a certain percentage. Our simulated results show that this model can simulate well the random walk of asset prices and explain the power-law tail distribution of returns, volatility clustering, jumps, and structural changes of asset prices

Application of stem cells in regeneration medicine

Abstract Regeneration is a complex process affected by many elements independent or combined, including inflammation, proliferation, and tissue remodeling. Stem cells is a class of primitive cells with the potentiality of differentiation, regenerate with self‐replication, multidirectional differentiation, and immunomodulatory functions. Stem cells and their cytokines not only inextricably linked to the regeneration of ectodermal and skin tissues, but also can be used for the treatment of a variety of chronic wounds. Stem cells can produce exosomes in a paracrine manner. Stem cell exosomes play an important role in tissue regeneration, repair, and accelerated wound healing, the biological properties of which are similar with stem cells, while stem cell exosomes are safer and more effective. Skin and bone tissues are critical organs in the body, which are essential for sustaining life activities. The weak repairing ability leads a pronounced impact on the quality of life of patients, which could be alleviated by stem cell exosomes treatment. However, there are obstacles that stem cells and stem cells exosomes trough skin for improved bioavailability. This paper summarizes the applications and mechanisms of stem cells and stem cells exosomes for skin and bone healing. We also propose new ways of utilizing stem cells and their exosomes through different nanoformulations, liposomes and nanoliposomes, polymer micelles, microspheres, hydrogels, and scaffold microneedles, to improve their use in tissue healing and regeneration

A Statistical Damage Area Evaluation Model of Asphalt Pavement Condition Based on Monte Carlo Testing Method

<div>Aimed at the problems that evaluation of pavement damage area in current standards is not clear enough, a current asphalt pavement condition evaluation model is introduced. If the repair area width reaches the pavement width, this should not be involved in pavement damage condition evaluation. Conversion coefficient K of repairing damage area should be 0.7. A Matlab testing method based on Mandonte Carlo algorithm is put forward to examine the rationality of</div><div>K being 0.7. The concrete conditions are as follows: First, input the known data to Matlab to fit, and then, derive the functions of elements obey affected pavement condition index, such as repair area, total crack length and other damage areas, etc., Last, calculate the corresponding PCI to get the result of pavement condition evaluation by using Monte Carlo algorithm. Through the comparison with the actual pavement conditions and the actual test, the rationality of K being 0.7 is verified.</div><div><br></div