Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
- pp 1283-1292
TLDR
Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.Abstract:
The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.read more
Citations
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Journal ArticleDOI
Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
TL;DR: Wang et al. as mentioned in this paper proposed a complete 3D framework to synthesize unlimited, realistic, and diverse motion data, which can provide various gaitrelated data, such as accelerometer data and depth map, not limited to silhouettes.
Journal ArticleDOI
Monocular Facial Performance Capture Via Deep Expression Matching
TL;DR: This paper presents a facial performance capture method that does not require facial scans and instead animates an artist-created model using standard blendshapes and gives artists high-level control over animations through a workflow similar to existing commercial solutions.
Journal ArticleDOI
A Motion Deblurring Disentangled Representation Network
TL;DR: Zhang et al. as mentioned in this paper presented a Motion Deblurring Disentangled Representation Network (MDDRNet), an end-to-end learned method for motion deblurring.
Book ChapterDOI
Unsupervised Domain Adaptation with Duplex Generative Adversarial Network
TL;DR: A novel duplex GAN (DupGAN) which extracts domain invariant and discriminative representation guided by bidirectional domain transformation, formulated as a GAN with duplex discriminators, indicating its effectiveness on unsupervised domain adaptation.
Posted Content
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling.
TL;DR: In this paper, an attention-based network is proposed for transferring motions between arbitrary objects, where dense similarities between the learned keypoints in the source and the driving images are computed in order to retrieve the appearance information from the source images.
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