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20172026
most citedLightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking

6 citations · 12 across the 14 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021

Semantic Relation Preserving Knowledge Distillation for Image-to-Image Translation

Zeqi Li, Ruowei Jiang, Parham Aarabi

Generative adversarial networks (GANs) have shown significant potential in modeling high dimensional distributions of image data, especially on image-to-image translation tasks. Ho…

cs.CV2021★ 1 cited

Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

Robin Kips, Ruowei Jiang, Sileye Ba +5

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. I…

cs.CV2021★ 2 cited

Continuous Face Aging via Self-estimated Residual Age Embedding

Zeqi Li, Ruowei Jiang, Parham Aarabi

Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial net…

cs.CV2021

LOHO: Latent Optimization of Hairstyles via Orthogonalization

Rohit Saha, Brendan Duke, Florian Shkurti +2

Hairstyle transfer is challenging due to hair structure differences in the source and target hair. Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LO…

cs.CV2021

The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation

Eu Wern Teh, Terrance DeVries, Brendan Duke +3

We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples…

cs.CV2021

SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

Brendan Duke, Abdalla Ahmed, Christian Wolf +2

In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable…