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Found 21 papers, 3 papers with code
InstructHumans: Editing Animated 3D Human Textures with Instructions
We present InstructHumans, a novel framework for instruction-driven 3D human texture editing.
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
We interact with the world with our hands and see it through our own (egocentric) perspective.
HiFiHR: Enhancing 3D Hand Reconstruction from a Single Image via High-Fidelity Texture
We present HiFiHR, a high-fidelity hand reconstruction approach that utilizes render-and-compare in the learning-based framework from a single image, capable of generating visually plausible and accurate 3D hand meshes while recovering realistic textures.
Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes.
Bias-Compensated Integral Regression for Human Pose Estimation
Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.
Improving Deep Regression with Ordinal Entropy
In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance.
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Cross-Domain 3D Hand Pose Estimation With Dual Modalities
To solve this problem, we present a framework for cross-domain semi-supervised hand pose estimation and target the challenging scenario of learning models from labelled multi-modal synthetic data and unlabelled real-world data.
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery
For monocular RGB-based 3D pose and shape estimation, multiple solutions are often feasible due to factors like occlusion and truncation.
Analyzing and Diagnosing Pose Estimation With Attributions
We present Pose Integrated Gradient (PoseIG), the first interpretability technique designed for pose estimation.
UV-Based 3D Hand-Object Reconstruction with Grasp Optimization
We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image.
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation
Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention.
Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image.
Dive Deeper Into Integral Pose Regression
We do a deep dive on the inference and back-propagation of integral pose regression to better understand the causes behind the performance and training differences.
Removing the Bias of Integral Pose Regression
Heatmap-based detection methods are dominant for 2D human pose estimation even though regression is more intuitive.
SemiHand: Semi-Supervised Hand Pose Estimation With Consistency
By design, we introduce data augmentation of differing difficulties, consistency regularizer, label correction and sample selection for RGB-based 3D hand pose estimation.
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding to the VSR techniques based on deep learning.
Cogradient Descent for Bilinear Optimization
Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure.
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).
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.
Aligning Latent Spaces for 3D Hand Pose Estimation
Hand pose estimation from monocular RGB inputs is a highly challenging task.
Disentangling Latent Hands for Image Synthesis and Pose Estimation
Hand image synthesis and pose estimation from RGB images are both highly challenging tasks due to the large discrepancy between factors of variation ranging from image background content to camera viewpoint.
