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20212026
most citedJitter Does Matter: Adapting Gaze Estimation to New Domains

4 citations · 6 across the 5 of their papers we have counts for

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cs.CV2026

RAGMesh with FaME-G2E: Long-Form Text-Driven 3D Face Generation and Editing

Hao Li, Ju Dai, Feng Zhou +6

Text-driven 3D face generation and editing remains challenging due to the difficulty of translating long-form descriptions into fine-grained facial geometry. Existing methods prima…

cs.CV20224 cited

Jitter Does Matter: Adapting Gaze Estimation to New Domains

Ruicong Liu, Yiwei Bao, Mingjie Xu +3

Deep neural networks have demonstrated superior performance on appearance-based gaze estimation tasks. However, due to variations in person, illuminations, and background, performa…

cs.CV2021

Separating Content and Style for Unsupervised Image-to-Image Translation

Yunfei Liu, Haofei Wang, Yang Yue +1

Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples. Existing works focus on disentangling domain-invariant content c…

cs.CV2021

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

Yunfei Liu, Ruicong Liu, Haofei Wang +1

Deep neural networks have significantly improved appearance-based gaze estimation accuracy. However, it still suffers from unsatisfactory performance when generalizing the trained…

cs.CV20212 cited

Vulnerability of Appearance-based Gaze Estimation

Mingjie Xu, Haofei Wang, Yunfei Liu +1

Appearance-based gaze estimation has achieved significant improvement by using deep learning. However, many deep learning-based methods suffer from the vulnerability property, i.e.…