Corpus ID: 174803447

Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking

  title={Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking},
  author={Tianxing Li and Zhi Yu and Edmund Phung and Brendan Duke and I. Kezele and P. Aarabi},
Recent works on convolutional neural networks (CNNs) for facial alignment have demonstrated unprecedented accuracy on a variety of large, publicly available datasets. However, the developed models are often both cumbersome and computationally expensive, and are not adapted to applications on resource restricted devices. In this work, we look into developing and training compact facial alignment models that feature fast inference speed and small deployment size, making them suitable for… Expand
Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example
  • Robin Kips, R. Jiang, +5 authors I. Bloch
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2021
This paper introduces an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. Expand


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The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations, and reduces the state-of-the-art failure rate by up to 70%. Expand
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This is the first attempt to create a tool suitable for annotating massive facial databases, and the tool for creating annotations for MultiPIE, XM2VTS, AR, and FRGC Ver. 2 databases is employed. Expand
Human Pose Estimation via Convolutional Part Heatmap Regression
A CNN cascaded architecture specifically designed for learning part relationships and spatial context, and robustly inferring pose even for the case of severe part occlusions is proposed, and achieves top performance on the MPII and LSP data sets. Expand
Interactive Facial Feature Localization
An improvement to the Active Shape Model is proposed that allows for greater independence among the facial components and improves on the appearance fitting step by introducing a Viterbi optimization process that operates along the facial contours. Expand
Deep Convolutional Network Cascade for Facial Point Detection
The proposed approach outperforms state-of-the-art methods in both detection accuracy and reliability and can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. Expand
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization. Expand
An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation
The Occluded Stacked Hourglass, based on the work of original StackedHourglass network for body pose joint estimation, is introduced, which is retrained to process a detected face window and output 68 occlusion heat maps, each corresponding to a facial landmark. Expand
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features. Expand
Fast R-CNN
  • Ross B. Girshick
  • Computer Science
  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deepExpand
Mask R-CNN
This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Expand