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20192026
most citedDomain Adaptation on Point Clouds via Geometry-Aware Implicits

65 citations · 177 across the 10 of their papers we have counts for

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cs.CV2026

HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion

Yating Wang, Kui Wu, Ran Yi +5

Modeling 3D hair strands is challenging due to complex hairstyle geometry, scarce high-quality training data, and incomplete or noisy input observations. Foundation models offer br…

cs.CV2024

MonoHair: High-Fidelity Hair Modeling from a Monocular Video

Keyu Wu, Lingchen Yang, Zhiyi Kuang +6

Undoubtedly, high-fidelity 3D hair is crucial for achieving realism, artistic expression, and immersion in computer graphics. While existing 3D hair modeling methods have achieved…

cs.CV2024

AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model

Beijia Chen, Yuefan Shen, Qing Shuai +3

Recent communities have seen significant progress in building photo-realistic animatable avatars from sparse multi-view videos. However, current workflows struggle to render realis…

cs.CV2021★ 65 cited

Domain Adaptation on Point Clouds via Geometry-Aware Implicits

Yuefan Shen, Yanchao Yang, Mi Yan +3

As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet u…

cs.CV2021★ 7 cited

DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

Yuefan Shen, Yanchao Yang, Youyi Zheng +2

We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic a…