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Open AccessProceedings ArticleDOI

Skeleton-Aided Articulated Motion Generation

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TLDR
This work makes the first attempt to generate articulated human motion sequence from a single image by utilizing paired inputs including human skeleton information as motion embedding and a single human image as appearance reference to generate novel motion frames based on the conditional GAN infrastructure.
Abstract
This work makes the first attempt to generate articulated human motion sequence from a single image. On one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets.

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Citations
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Proceedings ArticleDOI

Pose Transferrable Person Re-identification

TL;DR: A pose-transferrable person ReID framework which utilizes posetransferred sample augmentations (i.e., with ID supervision) to enhance ReID model training, and achieves great performance improvement, and outperforms most state-of-the-art methods without elaborate designing the ReIDs.
Proceedings ArticleDOI

Variational Convolutional Neural Network Pruning

TL;DR: Variational technique is introduced to estimate distribution of a newly proposed parameter, called channel saliency, based on which redundant channels can be removed from model via a simple criterion, and results in significant size reduction and computation saving.
Proceedings ArticleDOI

Towards Multi-Pose Guided Virtual Try-On Network

TL;DR: Li et al. as mentioned in this paper proposed a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses by using a conditional human parsing network to match both the desired pose and the desired clothes shape.
Book ChapterDOI

Deep Video Generation, Prediction and Completion of Human Action Sequences

TL;DR: In this paper, a two-stage framework is proposed to generate human action videos with no constraints or arbitrary number of constraints, which uniformly addresses the three problems: video generation given no input frames, video prediction given the first few frames, and video completion given the last and last frames.
Proceedings ArticleDOI

Deep Kinematics Analysis for Monocular 3D Human Pose Estimation

TL;DR: It is shown that optimizing the kinematics structure of noisy 2D inputs is critical to obtain accurate 3D estimations and targeted ablation study shows that each former step is critical for the latter one to obtain promising results.
References
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Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book ChapterDOI

U-Net: Convolutional Networks for Biomedical Image Segmentation

TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI

Generative Adversarial Nets

TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Posted Content

Image-to-Image Translation with Conditional Adversarial Networks

TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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