《3D Face Modeling from Diverse Raw Scan Data 2019》 《Pointnnet: Deep learning on point sets for 3d classification and segmeentation》 Learning on Meshes 第一个做法是用mesh auto encoder学习latent 3DMM representation 《Genearing 3D faces using convolutional mesh autoencoders 2018》 ...
为了解决这个问题,我们提出了一个新颖的编码解码器去直接在多个数据集的原始3D扫描数据中学习人脸模型,并建立他们之间的稠密链接。我们的方法提出了:基于PointNet的编码器,学习三维人脸的非线性身份和表达式潜在表示;一种对应的解码器可以为各种分辨率和expression建立稠密链接;该编码器可以嵌入现有的基于图像的编码器进行3D...
为了解决这些问题,本文提出了一个innovative framework,该框架主要是去做了以下两点:学习一个非线性的人脸模型从一系列不同的原始3D扫描数据集中;在这些扫描中建立点到点的稠密链接。具体来讲就是,通过将输入扫描视为无组织的点云,然后尝试使用PointNet框架将点云转化为身份和特征的表示,从解码器网络中恢复它的3D人脸...
To directly compare the behavioral HMM to the neural data, we visualized the activity of the neural populations tuned to different HMM states. We define a ‘trial’ as uninterrupted timepoints of the same state, and the response of a neuron on that trial as its average activity over those t...
3D Face Modeling From Diverse Raw Scan Data 标签(空格分隔): 论文 问题 这里稠密对应的局部拓扑三角形指的是什么意思 ? 摘要 传统的3D人脸模型使用线性子空间从单个数据集的有限扫描中学习人脸的潜在表达。从不同的3D数据集中构造一个大规模的人脸模型的主要困难在于原始扫描之间缺少紧密的对应关系。
论文:《Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data》 看到这个有点标题党的文章,我不仅要发出关于NLG落地的素质三连: 众所周知,对于自然语言处理中的NLG问题,一直都没有很好的落地场景,即便是目前最合适的“文本自动摘要-Auto Text Summarizatio...
Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar face textures is ...
Markovikj D, Gievska S, Kosinski M, Stillwell D (2013) Mining Facebook data for predictive personality modeling. In: Proceedings of the 7th international AAAI conference on weblogs and social media (ICWSM 2013), pp 23-26Markovikj, Dejan, Sonja Gievska, Michal Kosinski, and David Stillwell...
Currently, the process of scaling and adjusting the wire-frame model to fit the frontal facial image is not yet fully automated, and this represents one of the biggest problems in face modeling. In the next section, a solution to fully automate this process will be described in detail. ...
本文从如下的三个方面详细讲述了在Facebook搜索中应用Embedding检索技术遇到的挑战:modeling、serving以及full-stack optimization。modeling方面本文提出了一种统一的Embedding方式,也就是经典的双塔结构,一侧是query、user以及context特征;另一侧则是候选document。serving方面的系统架构图如下所示,本文采用Faiss库来进行embedding...