activity
20192026
most citedMesh Guided One-shot Face Reenactment using Graph Convolutional Networks

41 citations · 117 across the 19 of their papers we have counts for

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Showing 2020Show all

11 papers · 1 filter

cs.CV2020

NeuralMagicEye: Learning to See and Understand the Scene Behind an Autostereogram

Zhengxia Zou, Tianyang Shi, Yi Yuan +1

An autostereogram, a.k.a. magic eye image, is a single-image stereogram that can create visual illusions of 3D scenes from 2D textures. This paper studies an interesting question t…

cs.CV2020★ 24 cited

HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation

Xiaoyang Lyu, Liang Liu, Mengmeng Wang +5

Self-supervised learning shows great potential in monoculardepth estimation, using image sequences as the only source ofsupervision. Although people try to use the high-resolutioni…

cs.CV2020★ 1 cited

Stylized Neural Painting

Zhengxia Zou, Tianyang Shi, Shuang Qiu +2

This paper proposes an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image tr…

cs.CV2020★ 2 cited

Semi-Supervised Learning for In-Game Expert-Level Music-to-Dance Translation

Yinglin Duan, Tianyang Shi, Zhengxia Zou +6

Music-to-dance translation is a brand-new and powerful feature in recent role-playing games. Players can now let their characters dance along with specified music clips and even ge…

cs.CV2020★ 41 cited

Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks

Guangming Yao, Yi Yuan, Tianjia Shao +1

Face reenactment aims to animate a source face image to a different pose and expression provided by a driving image. Existing approaches are either designed for a specific identity…

cs.CV2020★ 4 cited

Dynamic Future Net: Diversified Human Motion Generation

Wenheng Chen, He Wang, Yi Yuan +2

Human motion modelling is crucial in many areas such as computer graphics, vision and virtual reality. Acquiring high-quality skeletal motions is difficult due to the need for spec…

cs.CV2020★ 6 cited

Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer

Xinhui Song, Tianyang Shi, Zunlei Feng +5

Facial action unit (AU) intensity is an index to describe all visually discernible facial movements. Most existing methods learn intensity estimator with limited AU data, while the…

cs.CV2020★ 1 cited

Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation

Tianyang Shi, Zhengxia Zou, Yi Yuan +1

With the rapid development of Role-Playing Games (RPGs), players are now allowed to edit the facial appearance of their in-game characters with their preferences rather than using…

cs.CV2020★ 1 cited

Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry

Yifan Xu, Tianqi Fan, Yi Yuan +1

Deep implicit field regression methods are effective for 3D reconstruction from single-view images. However, the impact of different sampling patterns on the reconstruction quality…

cs.CV2020★ 1 cited

Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization

Xinhui Song, Tianyang Shi, Tianjia Shao +3

The automatic intensity estimation of facial action units (AUs) from a single image plays a vital role in facial analysis systems. One big challenge for data-driven AU intensity es…

cs.CV2020★ 10 cited

Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks

Jiangke Lin, Yi Yuan, Tianjia Shao +1

3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lac…