Jin Lyu — accepted AI-conference papers

2026

4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

Jin Lyu, Liang An, Pujin Cheng, Yebin Liu, Xiaoying Tang

CVPR 2026

4D reconstruction of equine family (e.g. horses) from monocular video is important for animal welfare. Previous mainstream 4D animal reconstruction methods require joint optimization of motion and appearance over a whole video, which is time-consuming and sensitive to incomplete observation. In this

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2026

Monocular Mesh Recovery and Body Measurement of Female Saanen Goats

Bo Jin, ShichaoZhao, Jin Lyu, Bin Zhang, Tao Yu, Liang An +2

AAAI 2026technical

The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address

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