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Found 11 papers, 5 papers with code
Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow.
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
More precisely, we propose the pretext task of cross-view completion where the first input image is partially masked, and this masked content has to be reconstructed from the visible content and the second image.
PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling
It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information.
Leveraging MoCap Data for Human Mesh Recovery
In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains.
Ranked #3 on
3D Human Pose Estimation
on MPI-INF-3DHP
(Acceleration Error metric)
Deep Regression on Manifolds: A 3D Rotation Case Study
Many machine learning problems involve regressing variables on a non-Euclidean manifold -- e. g. a discrete probability distribution, or the 6D pose of an object.
SMPLy Benchmarking 3D Human Pose Estimation in the Wild
Predicting 3D human pose from images has seen great recent improvements.
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild
We introduce DOPE, the first method to detect and estimate whole-body 3D human poses, including bodies, hands and faces, in the wild.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk
While 3D object detection and pose estimation has been studied for a long time, its evaluation is not yet completely satisfactory.
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract.
Defining the Pose of any 3D Rigid Object and an Associated Distance
The pose of a rigid object is usually regarded as a rigid transformation, described by a translation and a rotation.
