SAOR:单视图铰接对象重建 SAOR: Single-View Articulated Object Reconstruction

作者:Mehmet Aygün Oisin Mac Aodha


We introduce SAOR, a novel approach for estimating the 3D shape, texture, andviewpoint of an articulated object from a single image captured in the wild.Unlike prior approaches that rely on pre-defined category-specific 3D templatesor tailored 3D skeletons, SAOR learns to articulate shapes from single-viewimage collections with a skeleton-free part-based model without requiring any3D object shape priors. To prevent ill-posed solutions, we propose across-instance consistency loss that exploits disentangled object shapedeformation and articulation. This is helped by a new silhouette-based samplingmechanism to enhance viewpoint diversity during training. Our method onlyrequires estimated object silhouettes and relative depth maps fromoff-the-shelf pre-trained networks during training. At inference time, given asingle-view image, it efficiently outputs an explicit mesh representation. Weobtain improved qualitative and quantitative results on challenging quadrupedanimals compared to relevant existing work.



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