8 citations · 11 across the 4 of their papers we have counts for
4 papers
SnapPose3D: Diffusion-Based Single-Frame 2D-to-3D Lifting of Human Poses
Alessandro Simoni, Riccardo Catalini, Davide Di Nucci +6
Depth ambiguity and joint uncertainty are the two main obstacles in obtaining accurate human pose predictions by 2D-to-3D lifting methods proposed in the literature. In particular,…
Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image
Yuki Kawana, Tatsuya Harada
We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image, focusing on part-level shape reconstruc…
Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects
Yuki Kawana, Yusuke Mukuta, Tatsuya Harada
Articulated objects exist widely in the real world. However, previous 3D generative methods for unsupervised part decomposition are unsuitable for such objects, because they assume…
Neural Star Domain as Primitive Representation
Yuki Kawana, Yusuke Mukuta, Tatsuya Harada
Reconstructing 3D objects from 2D images is a fundamental task in computer vision. Accurate structured reconstruction by parsimonious and semantic primitive representation further…