Robust feature detection and description are core components of 3D vision pipelines, yet they remain challenging under large viewpoint, scale, and appearance changes. Recent methods have improved local feature learning, but many still rely on convolutional operators whose local receptive fields limit their ability to c...
Gong-Lin Chen, Tian-Wen Fu, Hai-Wei Chen et al.· International Journal of Com...· 0 citations
Identity-preserving video generation aims to maintain a subject's identity while synthesizing realistic videos. Yet a single reference portrait captures the subject's appearance under only one facial configuration. As expressions change, facial appearance can vary in highly identity-specific ways, leaving the subject's...
Tian-Wen Fu, Wen-Bin Teng, Gong-Lin Chen et al.· 0 citations
Physical world modeling requires predicting how interactions change a scene, not merely generating coherent motion. We propose STRIKE, a framework that separates visual state transition learning from dense video generation. We construct event-aligned supervision by extracting observed states from training videos and pa...
Wen-Bin Teng, Tian-Shuo Xu, De-Pu Meng et al.· 0 citations
Illusory matches between distinct yet visually similar 3D surfaces--doppelgangers--remain a fundamental obstacle for large-scale, in-the-wild 3D reconstruction and visual localization. Prior work mitigates this issue with pairwise classifiers, but this design limits multi-view contextual reasoning and incurs O(n^2) inf...
Han-Yuan Xiao, Gong-Lin Chen, Hao-Lin Xiong et al.· 0 citations
XDG is introduced, an efficient visual disambiguation model designed for scalable SfM that remains competitive with the state-of-the-art disambiguation method across pairwise and reconstruction benchmarks and delivers more than a 3x inference speedup.
Gong-Lin Chen, Ben Southall, Han-Yuan Xiao et al.· 0 citations
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