Motion Capture of Hands in Action Using Discriminative Salient Points

By Luca Ballan, Aparna Taneja, Juergen Gall, Luc Van Gool, Marc Pollefeys. European Conference on Computer Vision, 2012

336 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/ec30e9550933c4a617ce78334d0e5977d7893d36

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

In Human-Computer Interaction

Citations in the corpus

Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data (2013, methodology): “24 mm on average which is close to the best offline methods [3].”
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition (2021, background): “Pioneer works in [1, 79, 80] investigate hand-hand and hand-object interactions relying on optimization frameworks which might be slow and difficult to tune.”
Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation (2016, methodology): “One of the most widely used shape models is the mesh surface [6], [37], [38] that is able to be deformed smoothly for articulate pose estimation.”
Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs (2016, methodology): “Such methods have demonstrated to be quite effective, especially with the depth cameras [3], [18], [19], [20], [21], [22].”
Survey on 3D Hand Gesture Recognition (2016, methodology): “[44] addressed the challenging problem of capturing the articulated motion of two hands that interact with each other and with an additional object.”
VirtualWorlds as Proxy for Multi-object Tracking Analysis (2016, background): “From early on, computer vision researchers leveraged 3D computer simulations to model articulated objects including human shape [8], face, and hand appearance [9], or even for scene interpretation and vision as inverse graphics [10, 11, 12].”
Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks (2014, methodology): “…the full pose of a human hand by ﬁtting convex polyhedra directly to range data using an approach inspired by constraint-based physics systems Ballan et al. [2012] show how to ﬁt high-polygon hand models to multiple camera views of a pair of hands interacting with a small sphere, using a…”
Resolving 3D Human Pose Ambiguities With 3D Scene Constraints (2019, background): “For the exact definition of Ψ and EPself (θ, β) we refer the reader to [8, 68].”
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild (2020, methodology): “To address this, we penalize poses that would human and/or object interpenetration using the collision loss L collision introduced in [7,62].”
Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation (2015, methodology): “The present work unifies the pose estimation for multiple synchronized RGB cameras (Ballan et al, 2012) and a monocular RGB-D camera (Tzionas et al, 2014).”
HOnnotate: A Method for 3D Annotation of Hand and Object Poses (2019, background): “Early approaches for joint hand+object pose estimation [2,38,65] typically relied on multi-view camera setups, and frame-by-frame tracking methods, which may require careful initialization and drift over time.”
Realtime and Robust Hand Tracking from Depth (2014, methodology): “Our accuracy is about 10 mm, which compares favorably to the accuracy (around 5 mm) reported on synthetic data [9, 13].”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, neutral): “For highly precise hand pose estimation, recent hybrid pipelines compliment data-driven per-frame reinitialization with model-based refinement (Taylor et al. 2016; Ballan et al. 2012; Sridhar et al. 2015; Qian et al. 2014; Ye et al. 2016).”
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation (2022, background): “[1] Luca Ballan, Aparna Taneja, Jürgen Gall, Luc Van Gool, and Marc Pollefeys.”
Cascaded hand pose regression (2015, background): “Several recent hand tracking works [21, 32, 26] provide a small number of real video sequences.”
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation (2016, background): “Many prior works have achieved good performance by different methods [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16].”