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20212026
most citedInstant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

26 citations · 39 across the 24 of their papers we have counts for

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cs.CV2026

TGRHuman: Text-Guided Realistic 3D Human Generation via Diffusion Renderer

Muxin Zhang, Chaohui Yu, Yuanwang Yang +3

Realistic 3D human generation plays a crucial role in many graphics applications. However, current methods still struggle to generate high-quality human geometry and texture while…

cs.CV2026

SCOPE: Scale-Consistent One-Pass Estimation of 3D Geometry

Zheng Zhang, Lihe Yang, Tianyu Yang +6

We present SCOPE (Scale-Consistent One-Pass Estimation of 3D Geometry), a novel approach for estimating 3D geometry from extended monocular video sequences, where existing methods…

cs.CV2026

Astra: a generalizable report generation foundation model for 3D computed tomography

Zhuhao Wang, Fang Chen, Chaohui Yu +19

Interpreting computed tomography (CT) requires review of hundreds of volumetric slices and remains time-intensive and expertise-dependent. Automated CT report generation offers a p…

cs.CV2026

AnimateAnyMesh++: A Flexible Feed-Forward Framework for High-Fidelity Text-Driven Mesh Animation

Zijie Wu, Chaohui Yu, Fan Wang +1

Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spa…

cs.CV2026

Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis

De-Xing Huang, Chaohui Yu, Xiao-Hu Zhou +8

X-ray angiography is the gold standard imaging modality for cardiovascular diseases. However, current deep learning approaches for X-ray angiogram analysis are severely constrained…

cs.CV2026

A Contrastive Pre-trained Foundation Model for Deciphering Imaging Noisomics across Modalities

Yuanjie Gu, Yiqun Wang, Chaohui Yu +4

Characterizing imaging noise is notoriously data-intensive and device-dependent, as modern sensors entangle physical signals with complex algorithmic artifacts. Current paradigms s…