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Found 17 papers, 3 papers with code
HTML: A Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization
3D hand reconstruction from images is a widely-studied problem in computer vision and graphics, and has a particularly high relevance for virtual and augmented reality.
MACS: Mass Conditioned 3D Hand and Object Motion Synthesis
To improve the naturalness of the synthesized 3D hand object motions, this work proposes MACS the first MAss Conditioned 3D hand and object motion Synthesis approach.
Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images
We propose a novel transformer-based framework that reconstructs two high fidelity hands from multi-view RGB images.
HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow
Through this, we demonstrate the quality of our probabilistic reconstruction and show that explicit ambiguity modeling is better-suited for this challenging problem.
RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video
Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions.
Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands.
Differentiable Event Stream Simulator for Non-Rigid 3D Tracking
This paper introduces the first differentiable simulator of event streams, i. e., streams of asynchronous brightness change signals recorded by event cameras.
EventHands: Real-Time Neural 3D Hand Pose Estimation from an Event Stream
Due to the different data modality of event cameras compared to classical cameras, existing methods cannot be directly applied to and re-trained for event streams.
Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation
We propose to use a model-based generative loss for training hand pose estimators on depth images based on a volumetric hand model.
Convex Optimisation for Inverse Kinematics
We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations.
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
Ranked #7 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Multi-Person Human Pose Estimation
3D Multi-Person Pose Estimation
+1
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
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)
3D Human Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+2
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence.
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images
We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset.
Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments.
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
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.
Fast and Robust Hand Tracking Using Detection-Guided Optimization
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.
