Search Results for author:
Found 20 papers, 5 papers with code
T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences
To address these issues, we introduce simple yet effective T2LM, a continuous long-term generation framework that can be trained without sequential data.
Purposer: Putting Human Motion Generation in Context
We present a novel method to generate human motion to populate 3D indoor scenes.
Placing Objects in Context via Inpainting for Out-of-distribution Segmentation
For example, we utilize it to augment Cityscapes samples by incorporating a subset of Pascal classes and demonstrate that models trained on such data achieve comparable performance to the Pascal-trained baseline.
SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction
Recent hand-object interaction datasets show limited real object variability and rely on fitting the MANO parametric model to obtain groundtruth hand shapes.
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions.
Reliability in Semantic Segmentation: Are We on the Right Track?
We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers and assess their reliability based on four metrics: robustness, calibration, misclassification detection and out-of-distribution (OOD) detection.
MonoNHR: Monocular Neural Human Renderer
Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas.
Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images
We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels.
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
Progressive Skeletonization: Trimming more fat from a network at initialization
Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance.
Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images
In this paper, we tackle the problem of 3D human shape estimation from single RGB images.
LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images
We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images.
3D Human Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+1
LCR-Net: Localization-Classification-Regression for Human Pose
We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images.
Ranked #4 on
3D Multi-Person Pose Estimation (root-relative)
on MuPoTS-3D
(MPJPE metric)
3D Human Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+4
Depth-Based Hand Pose Estimation: Data, Methods, and Challenges
To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
Understanding Everyday Hands in Action From RGB-D Images
We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification.
First-Person Pose Recognition Using Egocentric Workspaces
In egocentric views, hands and arms are observable within a well defined volume in front of the camera.
Depth-based hand pose estimation: methods, data, and challenges
To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
3D Hand Pose Detection in Egocentric RGB-D Images
We focus on the task of everyday hand pose estimation from egocentric viewpoints.
Egocentric Pose Recognition in Four Lines of Code
We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera.
