Search Results for author:
Found 27 papers, 21 papers with code
Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer
As a result, our CONTHO achieves state-of-the-art performance in both human-object contact estimation and joint reconstruction of 3D human and object.
Ranked #1 on
Contact Detection
on BEHAVE
URHand: Universal Relightable Hands
To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities.
Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery
Our two novel tokens are from a combination of separated two hand features; hence, it is much more robust to the distant token problem.
Ranked #1 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild
Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs).
Ranked #6 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New Dataset and Temporal Unfolding
In addition to the new dataset, we propose BlurHandNet, a baseline network for accurate 3D hand mesh recovery from a blurry hand image.
Bringing Inputs to Shared Domains for 3D Interacting Hands Recovery in the Wild
Hence, interacting hands of MoCap datasets are brought to the 2D scale space of single hands of ITW datasets.
Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation
Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection.
MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels
The action-conditioned methods generate a sequence of motion from a single action.
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.
3D Clothed Human Reconstruction in the Wild
Although much progress has been made in 3D clothed human reconstruction, most of the existing methods fail to produce robust results from in-the-wild images, which contain diverse human poses and appearances.
HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network
However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation.
Ranked #5 on
3D Hand Pose Estimation
on DexYCB
Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes
Second, we propose a joint-based regressor that distinguishes a target person's feature from others.
Ranked #10 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
2D Human Pose Estimation
3D Multi-Person Human Pose Estimation
+1
NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets
Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly supervised with GT 2D/3D joint coordinates of training sets.
Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation
Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain.
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video
Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy.
Ranked #58 on
3D Human Pose Estimation
on 3DPW
InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image
Therefore, we firstly propose (1) a large-scale dataset, InterHand2. 6M, and (2) a baseline network, InterNet, for 3D interacting hand pose estimation from a single RGB image.
Ranked #8 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image.
Ranked #6 on
3D Hand Pose Estimation
on FreiHAND
DeepHandMesh: A Weakly-supervised Deep Encoder-Decoder Framework for High-fidelity Hand Mesh Modeling
We design our system to be trained in an end-to-end and weakly-supervised manner; therefore, it does not require groundtruth meshes.
I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image
Most of the previous image-based 3D human pose and mesh estimation methods estimate parameters of the human mesh model from an input image.
Ranked #5 on
3D Hand Pose Estimation
on FreiHAND
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input.
PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose
This study presents a new network (i. e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system.
Ranked #49 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PCK metric)
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation
Multi-person pose estimation from a 2D image is challenging because it requires not only keypoint localization but also human detection.
PoseFix: Model-agnostic General Human Pose Refinement Network
In this paper, we propose a human pose refinement network that estimates a refined pose from a tuple of an input image and input pose.
Ranked #2 on
Multi-Person Pose Estimation
on MS COCO
(Validation AP metric)
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Ranked #5 on
Hand Pose Estimation
on HANDS 2017
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint.
Ranked #3 on
Pose Estimation
on ITOP front-view
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation
We propose a novel approach to 3D human pose estimation from a single depth map.
