基于Cult3D的交互式虚拟内耳显微解剖

Cult3D是一个实现基于普通电脑的交互式人体结构三维显示的有效平台。在对内耳结构三维数据处理的基础上，使用Cult3D Designer编辑器，采用组织事件、动作与对象的关联的编程手段实现了对内耳三维对象的复杂交互动作控制，完成基于普通电脑的交互式虚拟内耳显微解剖浏览系统。     

网络环境下数字商品三维虚拟展示技术分析

针对目前网络数字商品展示方面的不足,对虚拟3D模型及三维全景技术进行了研究,并对两种技术进行分析比较。结合二者的优点,该文提出了采用3DS Max与Cult3D技术相结合、全景技术与VR Toolbox相结合实现网上虚拟商品展示的设计方案,解决了目前电子商务应用的关键技术问题。     

网络环境下三维虚拟展示技术的应用研究

该文重点研究了网络环境下的三维虚拟展示技术,分别探讨了伪三维与web3D技术各自的优点与不足,并对两种展示方式进行分析与比较。在伪三维技术中对比了三维动画技术、360度全景图像技术和Flash3D技术。在Web3D技术中对VRML、View-point、Cult3D和Java3D进行阐述和对比。最后指出了web环境下三维虚拟展示技术的发展方向。     

网络环境下数字商品三维虚拟展示技术分析

针对目前网络数字商品展示方面的不足,对虚拟3D模型及三维全景技术进行了研究,并对两种技术进行分析比较。结合二者的优点,该文提出了采用3DS Max与Cult3D技术相结合、全景技术与VR Toolbox相结合实现网上虚拟商品展示的设计方案．解决了目前电子商务应用的关键技术问题。     

Web3D技术在远程教育中的应用

Web3D技术是虚拟现实技术的一种实现形式，在网络教育阶段采用具有交互功能的3D课件可大大提高学习效率．本文结合实际介绍了Cult3D技术以及基于Cult3D技术的多媒体课件的开发过程。     

基于虚拟现实技术的家电展示平台研究

研究了基于虚拟现实技术的家电展示平台,应用Cult3D技术进行交互设计。简述了虚拟现实技术以及Cult3D技术,家电平台设计的特点,框架以及部分实现。着重介绍了家电展示平台中的三维交互功能的实现,提高了家电的交互式虚拟展示布局的灵活性和视觉效果,能让用户及时、立体地观看到家电的虚拟现实效果。     

减速器虚拟拆装实验系统的构建

针对当前实验课程的设备台套数和场地等的不足,分析了国内外虚拟实验室的现状与特点,在研究Cult3D构建交互式三维虚拟场景的过程、方法和相关技术的基础上,以齿轮减速器为例,利用Cult3D及其它相关软件工具,构建了一个基于网络的齿轮减速器虚拟拆装实验,该实验系统具有三维、动态性、交互性、低带宽网络传输等特性,克服了传统实验的不足,在实际应用中取得了良好的效果。     

Cult3D入门——输出Cult3D模型场景

在三维软件中建立的三维模型、动画,需要输出成为Cult3D De-signer可以接受的C3D文件格式。Cult3D提供了3DSMAX和MAYA的插件使之能输出该文件格式。     

基于Cult3D与Ajax的三维网络商品组件的设计

介绍了常用的三维显示技术,针对网上购物的特点,采用Cult3D实现了网络商品的3D展现,并就如何实时安装、卸载自选商品配件、实时更换商品材质颜色以及实时变更观察角度等方面给出了相应的解决方案.同时,在网站交互上通过采用Ajax技术,提高了网站的响应速度,给客户提供良好的购物体验.     

Web3D技术在远程教育中的应用

Web3D技术是虚拟现实技术的一种实现形式,在网络教育阶段采用具有交互功能的3D课件可大大提高学习效率,本文结合实际介绍了Cult3D技术以及基于Cult3D技术的多媒体课件的开发过程。     

交互式三维虚拟校园的设计

文章应用图像处理和计算机图形学的知识，基于3DS Max和Cult3D软件平台，提出了一种交互式三维虚拟现实系统的设计方案。文章重点介绍了基于3DSMax的模型制作过程和基于Cult3D的交互功能设计过程，并以宁波大学为例完成了一个交互式三维虚拟校园的设计，取得了较好的实时性、交互性和良好的视觉效果。     

基于虚拟现实的水处理车间仿真的研究

为了达到在工人上岗前对其工作流程进行培训的目的,利用三维建模工具3DSMAX和虚拟现实仿真软件Cult 3D进行水处理车间工作流程的模拟.该文详细介绍了虚拟现实技术Cult 3D的功能,并将虚拟现实技术应用于大庆第二采油厂的水处理车间,详细分析了建模过程需要注意的问题以及如何模拟水处理车间工人操作的流程,同时分析了编程过程中的关键技术,开发了三维仿真动画程序.程序可以自动生成水处理车间的三维模型,并且以三维交互动画的形式演示操作流程.     

Cult3D在机电产品交互设计中的研究

说明了交互设计是一种在产品设计中易用,有效而让人愉悦的技术,在机电产品设计中引入交互设计,可更好地突出设计的作用,达到产品可用性的目的。分析了交互设计的含义、交互设计与虚拟现实技术间的关系,引出Cult3D在交互设计中的作用。通过对项目实例瓷砖生产线中高亮洁打蜡机的交互设计,说明了Cult3D在机电产品设计中的实用价值。     

基于Cult3D实现交互式虚拟现实网页

通过实例深入分析在3D MAX中制作三维模型、设置动画，运用Cuh 3D的事件和行为对三维模型进行交互式虚拟效果设计，并实现网上发布。     

基于语音驱动的三维人脸动画技术综述

随着三维数字虚拟人的发展,语音驱动三维人脸动画技术已经成为虚拟人交互的重要研究热点之一.其关键技术在于语音-视觉映射模型的建立以及三维人脸动画的合成.首先分析了音-视素匹配法和音-视觉参数映射两类方法的特点;之后阐述了目前三维人脸模型的建立方法,并依据三维人脸模型的表示方法不同,分析了不同运动控制方法的优缺点;然后阐述了语音驱动三维人脸动画的主观评价和客观评价方法;最后总结了语音驱动三维人脸动画技术的未来发展方向.     

基于Java与Cult3d技术的发动机运动仿真

为了解决发动机曲轴连杆机构复杂运动过程的虚拟仿真,研究了应用Java技术扩展Cult3D软件交互设计功能,从而实现了发动机曲轴连杆机构工作过程虚拟仿真。     

基于形变模型的真实感三维人脸动画研究

论文提出了一种新的基于三维人脸形变模型,并兼容于MPEG-4的三维人脸动画模型。采用基于均匀网格重采样的方法建立原型三维人脸之间的对齐,应用MPEG-4中定义的三维人脸动画规则,驱动三维模型自动生成真实感人脸动画。给定一幅人脸图像,三维人脸动画模型可自动重建其真实感的三维人脸,并根据FAP参数驱动模型自动生成人脸动画。     

A novel 2D and 3D multimodal approach for in-the-wild facial expression recognition

This study proposes a novel deep learning approach for the fusion of 2D and 3D modalities in in-the-wild facial expression recognition (FER). Different from other studies, we exploit the 3D facial information in in-the-wild FER. In particular, in-the-wild 3D FER dataset is not widely available; therefore, 3D facial data are constructed from available 2D datasets thanks to recent advances in 3D face reconstruction. The 3D facial geometry features are then extracted by deep learning technique to exploit the mid-level details, which provides meaningful expression for the recognition. In addition, to demonstrate the potential of 3D data on FER, the 2D projected images of 3D faces are taken as additional input to FER. These features are then jointly fused with 2D features obtained from the original input. The fused features are then classified by support vector machines (SVMs). The results show that the proposed approach achieves state-of-the-art recognition performances on Real-World Affective Faces (RAF) and Static Facial Expressions in the Wild (SFEW 2.0), and AffectNet dataset. This approach is also applied to a 3D FER dataset, i.e. BU-3DFE, to compare the effectiveness of reconstructed and available 3D face data for FER. This is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.     

AU-aware Algorithm for 3D Facial Reconstruction

As a critical task in computer vision and animation, 3D face reconstruction can provide 3D model structures and rich semantic information for multi-modal facial applications. However, monocular 2D facial images lack depth information and the parameters of the predicted facial model are not reliable, which causes poor reconstruction results. We propose to employ facial Action Unit (AU) and key facial points which are highly correlated with model parameters as a bridge to guide the regression of model-related parameters and thus solve the ill-posed monocular face reconstruction. Based on existing face reconstruction datasets, we provide a complete semi-automatic labeling scheme for facial AUs and construct a 300W-LP-AU dataset. Furthermore, a 3D face reconstruction algorithm based on AU awareness is put forward to realize end-to-end multi-tasking learning and reduce the overall training difficulty. Experimental results show that the algorithm can improve the face reconstruction performance, with high fidelity of the rebuilt facial model.     

Single-view-based 3D facial reconstruction method robust against pose variations

The 3D Morphable Model (3DMM) and the Structure from Motion (SfM) methods are widely used for 3D facial reconstruction from 2D single-view or multiple-view images. However, model-based methods suffer from disadvantages such as high computational costs and vulnerability to local minima and head pose variations. The SfM-based methods require multiple facial images in various poses. To overcome these disadvantages, we propose a single-view-based 3D facial reconstruction method that is person-specific and robust to pose variations. Our proposed method combines the simplified 3DMM and the SfM methods. First, 2D initial frontal Facial Feature Points (FFPs) are estimated from a preliminary 3D facial image that is reconstructed by the simplified 3DMM. Second, a bilateral symmetric facial image and its corresponding FFPs are obtained from the original side-view image and corresponding FFPs by using the mirroring technique. Finally, a more accurate the 3D facial shape is reconstructed by the SfM using the frontal, original, and bilateral symmetric FFPs. We evaluated the proposed method using facial images in 35 different poses. The reconstructed facial images and the ground-truth 3D facial shapes obtained from the scanner were compared. The proposed method proved more robust to pose variations than 3DMM. The average 3D Root Mean Square Error (RMSE) between the reconstructed and ground-truth 3D faces was less than 2.6 mm when 2D FFPs were manually annotated, and less than 3.5 mm when automatically annotated.