Skeleton-aware implicit function for single-view human reconstruction

The aim is to reconstruct a complete and detailed clothed human from a single-view input.Implicit function is suitable for this task because it represents fine shape details and varied topology.Current methods,however,often suffer from artefacts such as broken or disembodied body parts,missing details,or depth ambiguity due to the ambiguity and complexity of human articulation.The main issue observed by the authors is structureagnostic.To address these problems,the authors fully utilise the skinned multi-person linear(SMPL)model and propose a method using the Skeleton-aware Implicit Function(SIF).To alleviate the broken or disembodied body parts,the proposed skeleton-aware structure prior makes the skeleton awareness into an implicit function,which consists of a bone-guided sampling strategy and a skeleton-relative encoding strategy.To deal with the missing details and depth ambiguity problems,the authors’body-guided pixel-aligned feature exploits the SMPL to enhance 2D normal and depth semantic features,and the proposed feature aggregation uses the extra geometry-aware prior to enabling a more plausible merging with less noisy geometry.Additionally,SIF is also adapted to the RGB-D input,and experimental results show that SIF outperforms the state-of-the-arts methods on challenging datasets from Twindom and Thuman3.0.

Towards efficient and photorealistic 3D human reconstruction: A brief survey

Reconstructing 3D digital models of humans from sensory data is a long-standing problem in computer vision and graphics with a variety of applications in VR/AR,film production,and human–computer interaction,etc.While a huge amount of effort has been devoted to developing various capture hardware and reconstruction algorithms,traditional reconstruction pipelines may still suffer from high-cost capture systems and tedious capture processes,which prevent them from being easily accessible.Moreover,the dedicatedly hand-crafted pipelines are prone to reconstruction artifacts,resulting in limited visual quality.To solve these challenges,the recent trend in this area is to use deep neural networks to improve reconstruction efficiency and robustness by learning human priors from existing data.Neural network-based implicit functions have been also shown to be a favorable 3D representation compared to traditional forms like meshes and voxels.Furthermore,neural rendering has emerged as a powerful tool to achieve highly photorealistic modeling and re-rendering of humans by end-to-end optimizing the visual quality of output images.In this article,we will briefly review these advances in this fast-developing field,discuss the advantages and limitations of different approaches,and finally,share some thoughts on future research directions.

Cosmetic Safety Evaluation Based on In Vitro Three-dimensional Reconstructed Human Epidermis(3D-RHE) Models

Cosmetic safety evaluation employs a series of toxicological tests, on both qualitative and quantitative levels, to assess the potential risks for the daily use of selected cosmetic ingredients and final products. Traditionally, safety evaluation of cosmetics uses animal tests. With the development of in vitro science and the 3R （Reduction, Replacement and Refinement） principle, three-dimensional reconstructed human epidermis （3D-RHE） models have been developed and widely applied in cosmetic safety evaluation. Reconstructed human skin models possess anatomy and metabolism biology similar to real human tissue. This paper reviews the current application of 3D-RHE models in the safety evaluation of skin irritation, eye irritation, phototoxicity and genotoxicity potential of cosmetic ingredients/formulas. The advantages and disadvantages of using skin models are also discussed, and comments and suggestions are given for its future development.

Integrative multi-omics and systems bioinformatics in translational neuroscience:A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

回归与重构:智能时代的新知识观——再与陈丽教授等商榷

近年来,关于新知识观的讨论日趋热烈。陈丽教授等提出的“回归论知识观”深刻揭示了网络时代知识的回归现象,具有重大的理论和实践意义,但也存在某些值得商榷的问题与局限。人工智能聊天机器人ChatGPT的诞生,对人类的知识和学习带来多方面的影响。为探讨智能时代知识观发生的变化,文章将回归论知识观与重构主义知识观进行了比较分析,指出回归与重构是智能时代知识变化的双向趋势。认为硬知识的重要性进一步下降,软知识的重要性进一步上升;事实性知识和程序性知识的重要性下降,原理性知识的重要性上升;与个人关系不大的知识重要性下降,与个人关系密切的知识重要性上升;外语类知识的重要性下降,本土语言知识的重要性上升;联通在学习中的意义下降,零存整取式学习的价值提升。未来人类将以与生成式AI组成“复合脑”的方式进行人机合作式学习。

A Novel Method for Human Expression Rapid Reconstruction

Human expression rapid reconstruction has many potential applications in entertainment and social security. In this work, a rapid human expression measurement system based on a digital fringe projection and phase-shift technique is developed. The measurement system consists of a digital light processing (DLP) projector and a high-speed change-coupled device (CCD) camera. The DLP projector is used to project computer-generated fringe patterns onto the human face, and the high-speed CCD camera synchronized with the projector acquires the fringe images at a frame rate of 30 frames/s. Based on a three-step phase-shifting method and an accurate phase-height mapping algorithm, each frame of the 3-D human expression can be reconstructed. The principle of the proposed method is described and some experimental results are presented to demonstrate its performance. The experiment results show that the measurement system can reconstruct accurate 3-D human expression. An obvious merit of this method is that it can reconstruct the 3-D human expression in a very short time and it is not sensitive to the movement of the face during the measurement processing.

A Survey on Human Performance Capture and Animation

With the rapid development of computing technology, three-dimensional （3D） human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human body shapes and motions. Key research problems in human performance animation include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical effects. In this survey, according to the main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion simulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animation.