6 accepted papers
Articulated object pose estimation is a core task in embodied AI and computer vision. Existing methods typically regress poses in a continuous space, but often struggle with 1) navigating a large, complex search space and 2) failing to incorporate intrinsic kinematic constraints. In this paper, we i
Garments are common in daily life and are important for embodied intelligence community. Current category-level garments pose tracking works focus on predicting point-wise canonical correspondence and learning a shape deformation in point cloud sequences. In this paper, motivated by the 2D warping s…
Articulations are essential in everyday interactions, yet traditional RGB-based pose estimation methods often struggle with issues such as lighting variations and shadows. To overcome these challenges, we propose a novel Pre-defined keypoint based framework for category-level articulation pose estim
Human life is filled with articulated objects. Previous works for estimating the pose of category-level articulated objects rely on costly 3D point clouds or RGB-D images. In this paper, our goal is to estimate category-level articulation poses from a single RGB image, where we propose R2-Art, a nov…
IFL (Image Forgery Location) helps secure digital media forensics. However, many methods suffer from false detections (i.e., FPs) and inaccurate boundaries. In this paper, we proposed the CatmullRom Splines-based Regression Network (CSR-Net), which first rethinks the IFL task from the perspective of…
"Rigid and articulated objects are common in our daily lives. Pose estimation tasks for both types of objects have been extensively studied within their respective domains. However, a universal framework capable of estimating the pose of both rigid and articulated objects has yet to be reported. In…