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cs.CV2026
LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories
Zhanhao Liang, Tao Yang, Jie Wu +2
This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gradients through the differen…
cs.CV2026
Flexible Geometric Guidance for Probabilistic Human Pose Estimation with Diffusion Models
Francis Snelgar, Ming Xu, Stephen Gould +2
3D human pose estimation from 2D images is a challenging problem due to depth ambiguity and occlusion. Because of these challenges the task is underdetermined, where there exists m…
cs.CV2026
Gromov Wasserstein Optimal Transport for Semantic Correspondences
Francis Snelgar, Stephen Gould, Ming Xu +2
Establishing correspondences between image pairs is a long studied problem in computer vision. With recent large-scale foundation models showing strong zero-shot performance on dow…