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20232026
most citedTowards Explainable Fake Image Detection with Multi-Modal Large Language Models

2 citations · 4 across the 14 of their papers we have counts for

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cs.CV2026

FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-On

Shengyao Chen, Xianbing Sun, Liqing Zhang +1

Video virtual try-on aims to transfer a target garment onto a moving person across video frames. Current methods rely on human parsing masks or pose keypoints that frequently fail…

cs.CV2026

LoRC: Detecting AI-Generated Images via Low-Rank Collapse in Semantic Residuals

Haozhen Yan, Ruoxin Chen, Jiahui Zhan +6

Modern generators faithfully model macroscopic semantics, producing synthetic images that appear highly realistic. Consequently, decisive forensic cues reside in subtle non-semanti…

cs.CV2026

DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport

Xianbing Sun, Jiahui Zhan, Liqing Zhang +1

Recent diffusion- and flow-based VTON methods achieve strong results with pretrained generative models, but their reliance on multi-step sampling incurs high inference cost, while…

cs.CV2026

Enhancing Domain Generalization in 3D Human Pose Estimation through Controllable Generative Augmentation

Xinhao Hu, Yiyi Zhang, Liqing Zhang +1

Pedestrian motion, due to its causal nature, is strongly influenced by domain gaps arising from discrepancies between training and testing data distributions. Focusing on 3D human…

cs.CV2026

Any3DAvatar: Fast and High-Quality Full-Head 3D Avatar Reconstruction from Single Portrait Image

Yujie Gao, Yao Xiao, Xiangnan Zhu +4

Reconstructing a complete 3D head from a single portrait remains challenging because existing methods still face a sharp quality-speed trade-off: high-fidelity pipelines often rely…

cs.CV2026

Towards Source-Aware Object Swapping with Initial Noise Perturbation

Jiahui Zhan, Xianbing Sun, Xiangnan Zhu +4

Object swapping aims to replace a source object in a scene with a reference object while preserving object fidelity, scene fidelity, and object-scene harmony. Existing methods eith…