Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and
depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets …

Abstract We propose Neural-DynamicReconstruction (NDR), a template-free method to
recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

Given monocular videos, we build 3D models of articulated objects and environments
whose 3D configurations satisfy dynamics and contact constraints. At its core, our method …

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or
a collection of images. However, it is still challenging to reconstruct nonrigid structures from …

We introduce ViSER, a method for recovering articulated 3D shapes and dense3D
trajectories from monocular videos. Previous work on high-quality reconstruction of dynamic …

We aim to infer 3D shape and pose of objects from a single image and propose a learning-
based approach that can train from unstructured image collections, using only segmentation …

Recovering a skeletal shape from a monocular video is a longstanding challenge. Prevailing
nonrigid animal reconstruction methods often adopt a control-point driven animation model …

The advances in monocular 3D human pose estimation are dominated by supervised
techniques that require large-scale 2D/3D pose annotations. Such methods often behave …

Camera captured human pose is an outcome of several sources of variation. Performance of
supervised 3D pose estimation approaches comes at the cost of dispensing with variations …