ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.
The body-shape-conditioned models produce chairs which will be comfortable for a person with the given body shape; the pose-conditioned models produce chairs which accommodate the given sitting pose.
Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time.
We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.
We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images.
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects.
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision.
In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Ranked #2 on 6D Pose Estimation using RGBD on CAMERA25
In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force.
Our approach uses novel occlusion-robust pose-maps (ORPM) which enable full body pose inference even under strong partial occlusions by other people and objects in the scene.
Ranked #3 on 3D Multi-Person Pose Estimation (root-relative) on MuPoTS-3D (MPJPE metric)
We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence.
A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton.
Ranked #14 on 3D Human Pose Estimation on MPI-INF-3DHP
We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments.
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth.
In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time.