User-Specific Hand Modeling from Monocular Depth Sequences

Taylor, Jonathan; Stebbing, Richard V.; Ramakrishna, Varun; Keskin, Cem; Shotton, Jamie; Izadi, Shahram; Hertzmann, Aaron; Fitzgibbon, Andrew

doi:10.1109/cvpr.2014.88

Cited by 81 publications

(72 citation statements)

References 24 publications

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“…User specific models could be constructed [38], but would require a calibration stage. To avoid this, our approach optimises a generic model and refines the pose against such variance through RER.…”

Section: Kinematic Hand Modelmentioning

confidence: 99%

Guided optimisation through classification and regression for hand pose estimation

Krejov

Gilbert

Bowden

2017

Computer Vision and Image Understanding

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Section: Kinematic Hand Modelmentioning

confidence: 99%

Guided optimisation through classification and regression for hand pose estimation

Krejov

Gilbert

Bowden

2017

Computer Vision and Image Understanding

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“…Note that some special care has to be taken to allow the Levenberg-Marquardt algorithm to interact with a surface coordinate variable u ∈ U [16,2]. Such a variable has the atypical parameterization u = (τ, u, v) where τ is discrete (a triangle ID), and (u, v) are real valued coordinates in the unit triangle.…”

Section: Solvingmentioning

confidence: 99%

“…To deal with the inner minimizations, we follow the lead of [16,29] of defining a set of latent variables, passing them through the sums, and rewriting the energy in terms of a lifted energy defined over these additional latent variables. In our case, we have the ED deformation parameter sets…”

Section: Solvingmentioning

confidence: 99%

3D scanning deformable objects with a single RGBD sensor

Dou

Taylor

Fuchs

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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136

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We present a 3D scanning system for deformable objects that uses only a single Kinect sensor. Our work allows considerable amount of nonrigid deformations during scanning, and achieves high quality results without heavily constraining user or camera motion. We do not rely on any prior shape knowledge, enabling general object scanning with freeform deformations. To deal with the drift problem when nonrigidly aligning the input sequence, we automatically detect loop closures, distribute the alignment error over the loop, and finally use a bundle adjustment algorithm to optimize for the latent 3D shape and nonrigid deformation parameters simultaneously. We demonstrate high quality scanning results in some challenging sequences, comparing with state of art nonrigid techniques, as well as ground truth data.

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“…A binary latent tree model is used in Tang et al (2014) to guide the searching process of 3D locations of hand joints. For the related problem of videobased 3D hand tracking, a user-specific modeling method is proposed by Taylor et al (2014), while (Oikonomidis et al 2014) adopts an evolutionary optimization method to capture hand and object interactions. Leapmotion (2013) is a commercial system designed for close-range (within about 50 cm in depth) hand pose estimation.…”

Section: Related Workmentioning

confidence: 99%

Estimate Hand Poses Efficiently from Single Depth Images

Nanjappa

Zhang

et al. 2015

Int J Comput Vis

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This paper aims to tackle the practically very challenging problem of efficient and accurate hand pose estimation from single depth images. A dedicated two-step regression forest pipeline is proposed: given an input hand depth image, step one involves mainly estimation of 3D location and in-plane rotation of the hand using a pixelwise regression forest. This is utilized in step two which delivers final hand estimation by a similar regression forest model based on the entire hand image patch. Moreover, our estimation is guided by internally executing a 3D hand kinematic chain model. For an unseen test image, the kinematic model parameters are estimated by a proposed dynamically weighted scheme. As a combined effect of these proposed building blocks, our approach is able to deliver more precise estimation of hand poses. In practice, our approach works at 15.6 frame-per-second (FPS) on an average laptop when implemented in CPU, which is further sped-up to 67.2 FPS when running on GPU. In addition, we introduce and make publicly available a data-glove annotated depth image dataset covering various hand shapes and gestures, which enables us conducting quantitative analyses on real-world hand images. The effectiveness of our approach is verified empirically on both synthetic and the annotated real-world datasets for hand pose estimation, as well as related applications including part-based labeling and gesture classification. In addition to empirical studies, the consistency property of our approach is also theoretically analyzed.

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User-Specific Hand Modeling from Monocular Depth Sequences

Cited by 81 publications

References 24 publications

Guided optimisation through classification and regression for hand pose estimation

Guided optimisation through classification and regression for hand pose estimation

3D scanning deformable objects with a single RGBD sensor

Estimate Hand Poses Efficiently from Single Depth Images

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