Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images

By Yujun Cai, Liuhao Ge, Jianfei Cai, Junsong Yuan. 2018

318 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, 3D Vision & Pose, Human-Computer Interaction

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Citations in the corpus

Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications (2022, background): “One highly related direction is to model and estimate 3D hand joints [5,44,45,53,60,63,66,72,78,79,80,85,3] and mesh [26,27,51,55,75,83,46], object pose [14,28,29,35,43,57,64,71,76,81], or both [6,17,18,19,69].”
Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data (2020, methodology): “Second, most previous methods focus on predicting 3D joint positions [48, 34, 17, 3, 54].”
GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes (2020, background): “The problem of estimating 3D hand pose from monocular images has made major advances over the past few years [71, 61, 46, 10, 31, 50, 55].”
Reconstructing Hand-Object Interactions in the Wild (2020, background): “Early approaches in modeling hand and object require the input of multi-view image [31] or RGB-D sequence [51].”
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild (2020, background): “Numerous approaches have been proposed to regress a 3D pose from estimated 2D keypoints [62, 40, 32, 9].”
Two-hand Global 3D Pose Estimation using Monocular RGB (2020, background): “Note that [10, 6] leverage on depth data (more information than segmentation since the background has infinite depth) for training and [9, 5] utilize a deformable hand model and additional datasets.”
3D Hand Shape and Pose Estimation From a Single RGB Image (2019, methodology): “Following the same condition used in [63, 5, 44], we assume that the global hand scale and the absolute depth of root joint are provided at test time.”
Leveraging Photometric Consistency Over Time for Sparsely Supervised Hand-Object Reconstruction (2020, unknown): “Several approaches for hand pose estimation from RGB images focus on regressing 3D skeleton joint positions [5, 17, 26, 44, 52, 55].”
3D Hand Shape and Pose From Images in the Wild (2019, methodology): “Our method obtains state of-the-art results on standard benchmarks, even compared to methods using additional depth information in training [7, 47], camera intrinsics [27, 34], and post-processing optimization [27].”
Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge (2020, methodology): “Following the same protocol used in [5, 12, 40, 59], Settings Backbone EPE (RGB / Depth / KD)”
Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks (2019, methodology): “The first metric is the area under the curve (AUC) on the percentage of correct keypoints (PCK) score, which is a popular criterion to evaluate the pose estimation accuracy with different thresholds, as proposed in [3, 54].”
Learning Joint Reconstruction of Hands and Manipulated Objects (2019, unknown): “In contrast to other methods [4, 20, 37, 65, 80] that only predicts sparse skeleton keypoints, our model produces a dense hand mesh.”
Monocular Total Capture: Posing Face, Body, and Hands in the Wild (2018, methodology): “Recently, similar approaches have been introduced to predict 3D hand poses from a monocular view [74, 37, 12].”
Disentangling Latent Hands for Image Synthesis and Pose Estimation (2018, methodology): “3 and compare against state-of-the-art methods [3, 25, 40, 16, 21] on both the RHD and STB datasets.”
Hand Gesture Recognition Based on Computer Vision: A Review of Techniques (2020, methodology): “[89] RGB-D cameras – 3D hand pose estimation weakly supervised method mean error 0.”
FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration (2020, background): “Similarly, there have been noticeable achievements in 3D hand pose estimation from a depth input [34, 48, 45, 47, 53, 58] or a single RGB image [62, 9, 15, 8, 11, 61].”