Learning to Estimate 3D Hand Pose from Single RGB Images
Christian Zimmermann, Thomas Brox
University of Freiburg

arXiv:1705.01389v3 [] 15 Oct 2017

{zimmermann, brox}@cs.uni-freiburg.de

Abstract
Low-cost consumer depth cameras and deep learning
have enabled reasonable 3D hand pose estimation from single depth images. In this paper, we present an approach that
estimates 3D hand pose from regular RGB images. This task
has far more ambiguities due to the missing depth information. To this end, we propose a deep network that learns
a network-implicit 3D articulation prior. Together with detected keypoints in the images, this network yields good estimates of the 3D pose. We introduce a large scale 3D hand
pose dataset based on synthetic hand models for training
the involved networks. Experiments on a variety of test sets,
including one on sign language recognition, demonstrate
the feasibility of 3D hand pose estimation on single color
images.

Figure 1: Given a color image we detect keypoints in 2D
(shown overlayed) and learn a prior that allows us to estimate a normalized 3D hand pose.

sists of three deep networks that cover important subtasks
on the way to the 3D pose; see Figure 2. The first network
provides a hand segmentation to localize the hand in the image. Based on its output, the second network localizes hand
keypoints in the 2D images. The third network finally derives the 3D hand pose from the 2D keypoints, and is the
main contribution of this paper. In particular, we introduce
a canonical pose representation to make this learning task
feasible.
Another difficulty compared to 3D pose estimation at
the level of the human body is the restricted availability of
data. While human body pose estimation can leverage several motion capture databases, there is hardly any such data
for hands. To train a network, a large dataset with ground
truth 3D keypoints is needed. Since there is no such dataset
with sufficient variability, we created a synthetic dataset
with various data augmentation options.
The resulting hand pose estimation system yields very
promising results, both qualitatively and quantitatively on
existing small-scale datasets. We also demonstrate the use
of 3D hand pose for the task of sign language recognition.
The dataset and our trained networks are available online. 1

1. Introduction
The hand is the primary operating tool for humans.
Therefore, its location, orientation and articulation in space
is vital for many potential applications, for instance, object
handover in robotics, learning from demonstration, sign language and gesture recognition, and using the hand as an input device for man-machine interaction.
Full 3D hand pose estimation from single images is difficult because of many ambiguities, strong articulation, and
heavy self-occlusion, even more so than for the overall human body. Therefore, specific sensing equipment like data
gloves or markers are used, which restrict the application to
limited scenarios. Also the use of multiple cameras severly
limits the application domain. Most contemporary works
rely on the depth image from a depth camera. However,
depth cameras are not as commonly available as regular
color cameras, and they only work reliably in indoor environments.
In this paper, we present an approach to learn full 3D
hand pose estimation from single color images without the
need for any special equipment. We capitalize on the capability of deep networks to learn sensible priors from data
in order to resolve ambiguities. Our overall approach con-

2. Related work
2D Human Pose Estimation. Spurred by the MPII Human Pose benchmark [3] and the advent of Convolutional
1 https://lmb.informatik.uni-freiburg.de/projects/hand3d/

1

left/right
PosePrior
Crop
and
Resize
HandSegNet

PoseNet

Viewpoint

Figure 2: Our approach consists of three building blocks. First, the hand is localized within the image by a segmentation
network (HandSegNet). Accordingly to the hand mask, the input image is cropped and serves as input to the PoseNet. This
localizes a set of hand keypoints represented as score maps c. Subsequently, the PosePrior network estimates the most likely
3D structure conditioned on the score maps. This figure serves for illustration of the overall approach and does not reflect the
exact architecture of the individual building blocks.
Neural Networks (CNN) this field made large progress in
the last years. The CNN architecture of Toshev and Szegedy
[24] directly regresses 2D cartesian coordinates from color
image input. More recent works like Thompson et al. [22]
and Wei et al. [19] turned towards regressing score maps.
For parts of our work, we employ a comparable network
architecture as Wei et al. [19].
3D Human Pose Estimation. We only discuss the most
relevant works here and refer to Sarafianos et al. [17] for
more information. Like our approach, many works use a
two part pipeline [23, 7, 6, 21, 5]. First they detect keypoints in 2D to utilize the discriminative power of current
CNN approaches and then attempt to lift the set of 2D detections into 3D space. Different methods for lifting the representation have been proposed: Chen et al. [6] deployed a
nearest neighbor matching of a given 2D prediction using
a database of 2D to 3D correspondences. Tome et al. [21]
created a probabilistic 3D pose model based upon a mixture
of probabilistic PCA bases. Bogo et al. [5] optimizes the
reprojection error between 3D joint locations of a statistical
body shape model and 2D prediction. Pavlakos et al. [15]
proposed a volumetric approach that treats pose estimation
as per voxel prediction of scores in a coarse-to-fine manner, which gives a natural representation to the data, but is
computationally expensive and limited by the GPU memory
to fit the voxel grid. Recently, there have been several approaches that apply deep learning for lifting 2D keypoints to
3D pose for human body pose estimation [26, 11, 16]. Furthermore Mehta et al. [10] uses transfer learning to infer the
3D body pose directly from images with a single network.
While these works are all on 3D body pose estimation, we
provide the first such work for 3D hand pose estimation,
which is substantially harder due to stronger articulation
and self-occlusion, as well as less data being available.

Hand Pose Estimation. Athitsos and Sclaroff [4] proposed a single frame based detection approach based on
edge maps and Chamfer matching. With the advent of lowcost consumer depth cameras, research focused on hand
pose from depth data. Oikonomidis et al. [14] proposed
a technique based on Particle Swarm Optimization (PSO).
Sharp et al. [18] added the possibility for reinitialization.
A certain number of candidate poses is created and scored
against the observed depth image. Tompson et al. [22] used
a CNN for detection of hand keypoints in 2D, which is conditioned on a multi-resolution image pyramid. The pose in
3D is recovered by solving an inverse kinematics optimization problem. Approaches like Zhou et al. [27] or Oberweger et al. [12] train a CNN that directly regresses 3D coordinates given hand cropped depth maps. Whereas Oberweger et al. [12] explored the possibility to encode correlations between keypoint coordinates in a compressing bottleneck, Zhou et al. [27] estimate angles between bones of
the kinematic chain instead of Cartesian coordinates. Oberweger et al. [13] utilizes a CNN that can synthesize a depth
map from a given pose estimate. This allows them to successively refine initial pose estimates by minimizing the distance between the observed and the synthesized depth image.

There aren’t any approaches yet that tackle the problem
of 3D hand pose estimation from a single color image with a
learning based formulation. Previous approaches differ because they rely on depth data [22, 27, 12, 13], they use explicit models to infer pose by matching against a predefined
database of poses [4], or they only perform tracking based
on an initial pose rather than full pose estimation [14, 18].

Canonical Coordinates
wc
wrel = wc · R>
left/right

wrel

R
Viewpoint

Figure 3: Proposed architecture for the PosePrior network. Two almost symmetric streams estimate canonical coordinates
and the viewpoint relative to this coordinate system. Combination of the two predictions yields an estimation for the relative
normalized coordinates wrel .

3. Hand pose representation
Given a color image I ∈ RN ×M ×3 showing a single
hand, we want to infer its 3D pose. We define the hand pose
by a set of coordinates wi = (xi , yi , zi ), which describe the
locations of J keypoints in 3D space, i.e., i ∈ [1, J] with
J = 21 in our case.
The problem of inferring 3D coordinates from a single
2D observation is ill-posed. Among other ambiguities, there
is a scale ambiguity. Thus, we infer a scale-invariant 3D
structure by training a network to estimate normalized coordinates
1
winorm = · wi ,
(1)
s
where s = kwk+1 − wk k2 is a sample dependent constant
that normalizes the distance between a certain pair of keypoints to unit length. We choose k such that s = 1 for the
first bone of the index finger.
Moreover, we use relative 3D coordinates to learn a
translation invariant representation of hand poses. This is
realized by subtracting the location of a defined root keypoint. The relative and normalized 3D coordinates are given
by
wirel = winorm − wrnorm
(2)

estimating a score map for the center position of the human. The most likely location is used as center for a fixed
size crop. Since the hand size drastically changes across
images and depends much on the articulation, we rather
cast the hand localization as a segmentation problem. Our
HandSegNet is a smaller version of the network from Wei
et al. [19] trained on our hand pose dataset. Details on the
network architecture and its training prcedure are provided
in the supplemental material. The hand mask provided by
HandSegNet allows us to crop and normalize the inputs in
size, which simplifies the learning task for the PoseNet.

4.2. Keypoint score maps with PoseNet

where r is the root index. In experiments the palm keypoint
was the most stable landmark. Thus we use r = 0.

We formulate localization of 2D keypoints as estimation
of 2D score maps c = {c1 (u, v), . . . , cJ (u, v)}. We train a
network to predict J score maps ci ∈ RN ×M , where each
map contains information about the likelihood that a certain
keypoint is present at a spatial location.
The network uses an encoder-decoder architecture similar to the Pose Network by Wei et al. [19]. Given the image
feature representation produced by the encoder, an initial
score map is predicted and is successively refined in resolution. We initialized with the weights from Wei et al. [19],
where it applies, and retrained the network for hand keypoint detection. A complete overview over the network architecture is located in the supplemental material.

4. Estimation of 3D hand pose

4.3. 3D hand pose with the PosePrior network

We estimate three-dimensional normalized coordinates
wrel from a single input image. An overview of the general
approach is provided in Figure 2. In the following sections,
we provide details on its components.

The PosePrior network learns to predict relative, normalized 3D coordinates conditioned on potentially incomplete or noisy score maps c(u, v). To this end, it must learn
the manifold of possible hand articulations and their prior
probabilities. Conditioned on the score maps, it will output
the most likely 3D configuration given the 2D evidence.
Instead of training the network to predict absolute 3D coordinates, we rather propose to train the network to predict
coordinates within a canonical frame and additionally esti-

4.1. Hand segmentation with HandSegNet
For hand segmentation we deploy a network architecture
that is based on and initialized by the person detector of Wei
et al. [19]. They cast the problem of 2D person detection as

mate the transformation into the canonical frame. Explicitly
enforcing a representation that is invariant to the global orientation of the hand is beneficial to learn a prior, as we show
in our experiments in section 6.2.
Given the relative normalized coordinates we propose to
use a canonical frame wc , that relates to wrel in the following way: An intermediate representation
wc* = R(wrel ) · wrel

(3)

with R(wrel ) ∈ R3×3 being a 3D rotation matrix is calculated in a two step procedure. First, one seeks the rotation
Rxz around the x- and z-axis such that a certain keypoint
wac* is aligned with the y-axis of the canonical frame:
Rxz · wac* = λ · (0, 1, 0)> with λ ≥ 0.

(4)

Afterwards, a rotation Ry around the y-axis is calculated
such that
Ry · Rxz · woc* = (η, ζ, 0)

(5)

with η ≥ 0 for a specified keypoint index o. The total transformation between canonical and original frame is given by
R(wrel ) = Ry · Rxz .

(6)

In order to deal appropriately with the symmetry between
left and right hands, we flip right hands along the z-axis,
which yields the side agnostic representation
wic


=

c* c* >
(xc*
i , yi , zi )
c* c*
(xi , yi , −zic* )>

if its a left hand
if its a right hand

(7)

that resembles our proposed canonical coordinate system.
Given this canonical frame definition, we train our network
to estimate the 3D coordinates within the canonical frame
wc and separately to estimate the rotation matrix R(wrel ),
which we parameterize using axis-angle notation with three
parameters. Estimating the transformation R is equivalent
to predicting the viewpoint of a given sample with respect
to the canonical frame. Thus, we refer to the problem as
viewpoint estimation.
The network architecture for the pose prior has two parallel processing streams; see Figure 3. The streams use
an almost identical architecture given in the supplementary.
They first process the stack of J score maps in a series of
6 convolutions with ReLU non-linearities. Information on
whether the image shows a left or right hand is concatenated with the feature representation and processed further
by two fully-connected layers. The streams end with a fullyconnected layer with linear activation, which yields estimations for viewpoint R and canonical coordinates wc . Both
estimations combined lead to an estimation of wrel .

4.4. Network training
For training of HandSegNet we apply standard softmax
cross-entropy loss and L2 loss for PoseNet. The PosePrior
network uses two loss terms. First a squared L2 loss for the
canonical coordinates
c
c
Lc = wgt
− wpred

2
2

(8)

c
and the ground truth
based on the network predictions wpred
c
wgt . Secondly, a squared L2 loss is imposed on the canonical transformation matrix:
2

Lr = kRpred − Rgt k2 .

(9)

The total loss function is the unweighted sum of Lc and Lr .
We used Tensorflow [2] with the Adam solver [9] for
training. Details on the learning procedure are in the supplementary material.

5. Datasets for hand pose estimation
5.1. Available datasets
There are two available datasets that apply to our problem, as they provide RGB images and 3D pose annotation.
The so-called Stereo Hand Pose Tracking Benchmark [25]
provides both 2D and 3D annotations of 21 keypoints for
18000 stereo pairs with a resolution of 640 × 480. The
dataset shows a single person’s left hand in front of 6 different backgrounds and under varying lighting conditions. We
divided the dataset into an evaluation set of 3000 images
(S-val) and a training set with 15000 images (S-train).
Dexter [20] is a dataset providing 3129 images showing
two operators performing different kinds of manipulations
with a cuboid in a restricted indoor setup. The dataset provides color images, depth maps, and annotations for fingertips and cuboid corners. The color images have a spatial
resolution of 640×320. Due to the incomplete hand annotation, we use this dataset only for investigating the crossdataset generalization of our network. We refer to this test
set as Dexter.
We downsampled both datasets to a resolution of 320×
240 to be compatible with our rendered dataset. We transform our results back to coordinates in the original resolution, when we report pixel accuracies in the image domain.
The NYU Hand Pose Dataset by Tompson et al. [22],
commonly used for hand pose estimation from depth images, does not apply to a color based approach, because
only registered color images are provided. In the supplementary we show more evidence why this dataset cannot be
used for our task.

5.2. Rendered hand pose dataset
The above datasets are not sufficient for training a deep
network due to limited variation, number of available samples, and partially incomplete annotation. Therefore, we

GT
Net
Figure 4: Our new dataset provides segmentation maps with
33 classes: three for each finger, palm, person, and background. The 3D kinematic model of the hand provides 21
keypoints per hand: 4 keypoints per finger and one keypoint
close to the wrist.

complement them with a new dataset for training. To avoid
the known problem of poor labeling performance by human
annotators in three-dimensional data, we utilize freely available 3D models of humans with corresponding animations
from Mixamo 2 . Then we used the open source software
Blender 3 to render images. The dataset is publicly available online.
Our dataset is built upon 20 different characters performing 39 actions. We split the data into a validation set (R-val)
and a training set (R-train), where a character or action can
exclusively be in one of the sets but not in the other. Our
proposed split results into 16 characters performing 31 actions for training and 4 characters with 8 actions in the validation set.
For each frame we randomly sample a new camera location, which is roughly located in a spherical vicinity around
one of the hands. All hand centers lie approximately in
a range between 40cm and 65cm from the camera center.
Both left and right hands are equally likely and the camera
is rotated to ensure that the hand is at least partially visible
from the current viewpoint. After the camera location and
orientation are fixed, we randomly sample one background
image from a pool of 1231 background images downloaded
from Flickr 4 . Those images show different kinds of scenes
from cities and landscapes. We ensured that they do not
contain persons.
To maximize the visual diversity of the dataset, we randomize the following settings for each rendered frame: we
apply lighting by 0 to 2 directional light sources and global
illumination, such that the color of the sampled background
image is roughly matched. Additionally we randomize light
positions and intensities. Furthermore, we save our renderings using a lossy JPG compression with the quality factor
being randomized from no compression up to 60%. We also
randomized the effect of specular reflections on the skin.
2 http://www.mixamo.com
3 http://www.blender.org
4 http://www.flickr.com

R-val
S-val
R-val
S-val
Dexter

AUC
0.724
0.817
0.635
0.762
0.489

EPE median
5.001
5.013
6.745
5.528
13.684

EPE mean
9.135
5.522
18.741
18.581
25.160

Table 1: The top rows (GT) report performance for the
PoseNet operating on ground truth cropped hand images.
The bottom rows (Net) show results when the hand crops
are generated using HandSegNet. PoseNet was trained
jointly on R-train and S-train, whereas HandSegNet was
only trained on R-train. End point errors are reported in
pixels with respect to the uncropped image and AUC is calculated over an error range from 0 to 30 pixels.

In total our dataset provides 41258 images for training and 2728 images for evaluation with a resolution of
320×320 pixels. All samples come with full annotation of
a 21 keypoint skeleton model of each hand and additionally
33 segmentation masks are available plus the background.
As far as the segmentation masks are concerned there is a
class for the human, one for each palm and each finger is
composed by 3 segments. Figure 4 shows a sample from
the dataset. Every finger is represented by 4 keypoints: the
tip of the finger, two intermediate keypoints and the end located on the palm. Additionally, there is a keypoint located
at the wrist of the model. For each of the hand keypoints,
there is information if it is visible or occluded in the image.
Also keypoint annotations in the camera pixel coordinate
system and in camera centered world coordinates are given.
The camera intrinsic matrix and a ground truth depth map
are available, too, but were not used in this work.

6. Experiments
We evaluated all relevant parts of the overall approach:
(1) the detection of hand keypoints of the PoseNet with and
without the hand segmentation network; (2) the 3D hand
pose estimation and the learned 3D pose prior. Finally, we
applied the hand pose estimation to a sign language recognition benchmark.

6.1. Keypoint detection in 2D
Table 1 shows the performance of PoseNet on 2D keypoint estimation. We report the average endpoint error
(EPE) in pixels and the area under the curve (AUC) on the
percentage of correct keypoints (PCK) for different error
thresholds; see Figure 6.
We evaluated two cases: one using images, where the
hand is cropped with the ground truth oracle (GT), and one
using the predictions from HandSegNet for cropping (Net).

Figure 5: Exemplary 2D keypoint localization results. The first two columns show samples from Dexter, the following three
depict R-val and the last one are samples from S-val.

0.8

R-train
R-val

PCK

0.6
0.4
R-train (AUC=0.44)
S-train (AUC=0.39)
Jointly (AUC=0.49)

0.2
0

0

10

20

30

Direct
20.2 9.2%
20.9 11.2%

Bottleneck
21.1 14%
21.9 16%

Local
35.1 90%
39.1 108%

NN
0.0 −100%
26.9 43%

Prop.
18.5
18.8

Table 2: Average median end point error per keypoint of
the predicted 3D pose for different lifting approaches given
a noisy ground truth 2D pose. Networks were trained on
R-train. The results are reported in mm and the subscript
gives the relative performance to the proposed approach.

Threshold in px
Figure 6: Results on 2D keypoint estimation when using
different training sets for PoseNet. Shown is the percentage
of correct keypoints (PCK) over a certain threshold in pixels
evaluated on Dexter. Jointly training on R-train and S-train
yields the best results.

The first case shows the performance of PoseNet in isolation, while the second shows the performance of the complete 2D keypoint estimation. The difference between the
median and the mean for the latter case show that HandSegNet is reliable in most cases but is sometimes not able to
segment the hand correctly, which makes the 2D keypoint
prediction fail.
The results show that the method works on our synthetic
dataset (R-val) and the stereo dataset (S-val) equally well.
The Dexter dataset is more difficult because the dataset is
different from the training set and because of frequent occlusions of the hand by the handled cube. We did not have
samples with occlusion (apart from self-occlusion) in the
training set.
In Figure 6 we show that training on more diverse data
helps cross-dataset generalization. While training only on
our synthetic dataset R-train yields much better results on
Dexter than training on the limited stereo dataset S-train,
training on R-train and S-train together yields the best results. Figure 5 shows some qualitative results of this configuration. Additional examples are in the supplementary.

6.2. Lifting the estimation to 3D
6.2.1

Pose representation

We evaluated the proposed canonical frame representation
for predicting the 3D hand pose from 2D keypoints by comparing it to several alternatives. All variants share a common base architecture that is identical to one stream of the
PosePrior proposed in 4.3. They were trained on score
maps c with a spatial resolution of 32 by 32 pixels. To
avoid overfitting, we augmented the score maps by disturbing the keypoint location with Gaussian noise of variance
1.5 pixel. Additionally the scoremaps are randomly scaled
and translated. Table 2 shows the resulting end point errors
per keypoint.
The Direct approach tries to lift the 2D keypoints directly
to the full 3D coordinates wrel without using a canonical
frame. This is disadvantageous, because it is difficult for
the network to learn separate the global rotation of the hand
from the articulation.
The Bottleneck approach is inspired by Oberweger et
al. [12], who introduced a bottleneck layer before estimating the coordinates. We inserted an additional FC layer before the final FC output layer, parameterize it as in Oberweger et al. with 30 channels and linear activation. The
outcome was not better than with the Direct approach.

Figure 7: Qualitative examples of our complete system. Input to the network are color image and the information if its a left
or right hand. The network estimates the hand segmentation mask, localizes keypoints in 2D and outputs the most likely 3D
pose. The samples on the left hand side are from a dataset we recorded for qualitative evaluation, on the top right hand side
is a sample from the sign language dataset and the bottom right sample is taken from S-val. In the supplementary material
we provide more qualitative examples.
1

PCK

0.8
PSO (AUC=0.709)
ICPPSO (AUC=0.748)
CHPR (AUC=0.839)
[26] (AUC=0.770)
Ours (AUC=0.948)

0.6
0.4
20

30

40

50

threshold in mm

Figure 8: The left most column shows the input image as
gray scale with the input score map overlayed as red dots.
Every row corresponds to a separate forward pass of the network. The two columns to the right visualize the predicted
3D structure of the network from different viewpoints in
canonical coordinates. Ground truth is displayed in dashed
green and the network prediction is shown in solid red.

The Local approach incorporates the kinematic model of
the hand and uses the network to estimate articulation parameters of the model. We generalize [27] by estimating
not only the angles but also the bone length. The network is
trained to estimate two angles and one length per keypoint,
which results in 63 parameters. The angles express rotations in a bone local coordinate system. This approach only
works if the hand is always shown from the same direction,
but cannot capture the global pose of the hand.
Finally, the NN approach matches the 2D keypoints to

Figure 9: Results for our complete system on S-val compared to approaches from [25] and [26]. Shown is the percentage of correct keypoints (PCK) over respective thresholds in mm. PoseNet and PosePrior are trained on S-train
and R-train, whereas the HandSegNet is trained on R-train.

the most similar sample from the training set and retrieves
the 3D coordinates from this sample [6]. While this approach trivially works best on the training set, it does not
generalize well to new samples.
The generalization of the other approaches is quite good
showing similar errors for both the training and the validation set. The proposed approach from 4.3 worked best and
was used for the following experiments.
6.2.2

Analysis of the learned prior

To examine the 3D prior learned by the network we input
score maps that lack keypoints and Figure 8 shows the 3D
pose prediction from two different viewpoints. The extreme
case, with no keypoints provided as input at all, shows the
canonical prior learned by the network. As more keypoints

are added, the network adjusts the predicted pose to this
additional evidence. This experiment also simulates the situation of occluded 2D keypoints and demonstrates that the
learned prior allows the network to still retrieve reasonable
poses.
6.2.3

Comparison to literature

Since there is no work on 3D hand pose estimation from
RGB images yet, we cannot compare to alternative approaches. To still relate our results coarsely to literature,
we compare them to Zhang et al. [25], who provide results
in mm for state-of-the-art 3D hand pose tracking on depth
data. They run their experiments on the stereo dataset Sval, which also contains RGB images. Since in contrast to
Zhang et al. our approach does not use the depth data, it
still comes with ambiguities with regard to scale and absolute depth. Thus, we accessed the absolute position of the
root keypoint and the scale of the hand to shift and scale
our predicted 3D hand pose, which yields metric world coordinates w by using (1) and (2). For this experiment we
trained PosePrior on score maps predicted by PoseNet using the same schedule as for the experiment in section 6.2.2.
PoseNet is trained separately as described in 6.1 and then
kept fixed. Figure 9 shows that our approach largely outperforms the approaches presented in Zhang et al. [25] although we use the depth map only for rescaling and shifting
in the end. Additionally we report results of the lifting approach presented by Zhao et al. [26] in conjunction with our
PoseNet, which we train in a similar manner. Results are
inferior to the proposed PosePrior. We believe the reason
is that using score maps as input for the lifting is advantageous over coordinates, because it can handle ambiguities in
hand keypoint detection. Qualitative 3D examples on three
different datasets with the complete processing pipeline are
shown in Figure 7.

6.3. Sign language recognition
Previous hand pose estimation approaches depending on
depth data cannot be applied to most sign language recognition datasets, as they only come with color images. As a
last exemplary experiment, we used our hand pose estimation system and trained a classifier for gesture recognition
on top of it. The classifier is a fully connected three layer
network with ReLU activation functions; c.f. the supplemental material for the network details.
We report results on the so-called RWTH German Fingerspelling Database [8]. It contains 35 gestures representing the letters of the alphabet, German umlauts, and the
numbers from one to five. The dataset comprises 20 different persons, who did two recordings each for every gesture.
Most of the gestures are static except for the ones for the
letters J, Z, Ä, Ö, and Ü, which are dynamic. In order to

Method
Dreuw et al. [8]
Dreuw on subset [1]
Ours 3D

Word error rate
35.7 %
36.56 %
33.2 %

Table 3: Word error rates in percent on the RWTH German Fingerspelling Database subset of non dynamic gestures. Results for Dreuw et al. [8] on the subset from [1].
keep this experiment simple, we ran the experiments on the
subset restricted to 30 static gestures.
The database contains recordings by two different cameras, but we used only one camera. The short video sequences have a resolution of 320×240 pixels. We used the
middle frame from each video sequence as color image and
the gesture class labels as training data. This dataset has
1160 images, which we separated by signers into a validation set with 232 images and a training set with 928 images. We resized image to 320 × 320 pixels and trained
on randomly sampled 256 × 256 crops. Because the images were taken from a compressed video stream they exhibit significant compression artifacts previously unseen by
our networks. Thus, we labeled 50 images from the training set with hand keypoints, which we used to fine-tune
our PoseNet upfront. Afterwards the pose estimation part
is kept fixed and we solely train the GestureNet. Table 3
shows that our system archives comparable results to Dreuw
et al. [8] on the subset of gestures we used for the comparison.

7. Conclusions
We have presented the first learning based system to estimate 3D hand pose from a single image. We contributed
a large synthetic dataset that enabled us to train a network
successfully on the task. We have shown that the network
learned a 3D pose prior that allows it to predict reasonable
3D hand poses from 2D keypoints in real world images.
While the performance of the network is even competitive
to approaches that use depth maps, there is still much room
for improvements. The performance seems mostly limited
by the lack of an annotated large scale dataset with realworld images and diverse pose statistics.

Acknowledgements
We gratefully acknowledge funding by the BadenWürttemberg Stiftung as part of the projects ROTAH and
RatTrack. Also we thank Nikolaus Mayer, Benjamin Ummenhofer and Maxim Tatarchenko for valuable ideas and
many fruitful discussions.

References
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Supplementary Material:
Learning to Estimate 3D Hand Pose from Single RGB Images
Christian Zimmermann, Thomas Brox
University of Freiburg
{zimmermann, brox}@cs.uni-freiburg.de

A. HandSegNet architecture and learning
schedule

id
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

Table 4 contains the architecture used for HandSegNet. It
was trained for hand segmentation on R-train with a batch
size of 8 and using ADAM solver [9]. The network was
initialized using weights of Wei et al. [19] for layers 1 to
16 and then trained for 40000 iterations using a standard
softmax cross-entropy loss. The learning rate was 1 · 10−5
for the first 20000 iterations, 1 · 10−6 for following 10000
iterations and 1 · 10−7 until the end. Except for random
color hue augmentation of 0.1 no data augmentation was
used. From the 320×320 pixel images of the training set a
256×256 crop was taken randomly.

B. PoseNet architecture and learning schedule
Table 5 contains the architecture used for PoseNet. In all
cases it was trained with a batch size of 8 and using ADAM
solver [9]. The initial 16 layers of the network are initialized
using weights of Wei et al. [19] all others are randomly initialized . The network is trained for 30000 iterations using a
L2 loss. The learning rate is 1 · 10−4 for the first 10000 iterations, 1 · 10−5 for following 10000 iterations and 1 · 10−6
until the end. For ground truth generation of the score maps
we use normal distributions with a variance of 25 pixels and
the mean being equal to the given keypoint location. We
normalize the resulting maps such that each map contains
values from 0 to 1, if there is a keypoint visible. For invisible keypoints the map is zero everywhere.
We train PoseNet on axis aligned crops that are resized
to a resolution of 256×256 pixels by bilinear interpolation.
The bounding box is chosen such that all keypoints of a single hand are contained within the crop. We augment the
cropping procedure by modifying the calculated bounding
box in two ways. First, we add noise to the calculated center of the bounding box, which is sampled from a zero mean
normal distribution with variance of 10 pixels. The size of
the bounding box is changed accordingly to still contain all
hand keypoints. Second we find it helpful to improve generalization performance by adding a bit of noise on the coor-

Name
Input image
Conv. + ReLU
Conv. + ReLU
Maxpool
Conv. + ReLU
Conv. + ReLU
Maxpool
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Maxpool
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv.
Bilinear Upsampling
Argmax
Hand mask

Kernel
3×3
3×3
4×4
3×3
3×3
4×4
3×3
3×3
3×3
3×3
4×4
3×3
3×3
3×3
3×3
3×3
1×1
-

Dimensionality
256×256×3
256×256×64
256×256×64
128×128×64
128×128×128
128×128×128
64×64×128
64×64×256
64×64×256
64×64×256
64×64×256
32×32×256
32×32×512
32×32×512
32×32×512
32×32×512
32×32×512
32×32×2
256×256×2
256×256×1
256×256×1

Table 4: Network architecture of the proposed HandSegNet
network. Except for input and hand mask output every row
of the table gives a data tensor of the network and the operations that produced it.

dinates used to generate the score maps. Therefore, we add
a normal distribution of zero mean and variance 1.5 to the
ground truth keypoint coordinates, whereas each keypoint
is sampled independently. Additionally we apply random
contrast augmentation with a scaling factor between 0.5 and
1.0, which is sampled from a uniform distribution.

C. PosePrior architecture
Table 6 contains the architecture used for each stream of
the PosePrior. It uses 6 convolutional layers followed by
10

id
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

Name
Input image
Conv. + ReLU
Conv. + ReLU
Maxpool
Conv. + ReLU
Conv. + ReLU
Maxpool
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Maxpool
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv.
Concat(16, 17)
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv.
Concat(16, 17, 24)
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv.

Kernel
3×3
3×3
4×4
3×3
3×3
4×4
3×3
3×3
3×3
3×3
4×4
3×3
3×3
3×3
3×3
3×3
1×1
7×7
7×7
7×7
7×7
7×7
1×1
7×7
7×7
7×7
7×7
7×7
1×1

Dimensionality
256×256×3
256×256×64
256×256×64
128×128×64
128×128×128
128×128×128
64×64×128
64×64×256
64×64×256
64×64×256
64×64×256
32×32×256
32×32×512
32×32×512
32×32×512
32×32×512
32×32×512
32×32×21
32×32×533
32×32×128
32×32×128
32×32×128
32×32×128
32×32×128
32×32×21
32×32×554
32×32×128
32×32×128
32×32×128
32×32×128
32×32×128
32×32×21

Table 5: Network architecture of the PoseNet network. Except for input every row of the table represents a data tensor
of the network and the operations that produced it. Outputs
of the network are are predicted score maps c from layers
17, 24 and 31.

two fully-connected layers. All use ReLU activation function and the fully-connected layers have a dropout probability of 0.2 to randomly drop a neuron. Preceeding to the first
FC layer, information about the hand side is concatenated
to the flattened feature representation calculated by the convolutional layers. All drops in spatial dimension are due
to strided convolutions. The network ceases with a fullyconnected layer that estimates P parameters, where P = 3
for Viewpoint estimation and P = 63 for the coordinate
estimation stream.

id

Name
Input
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Conv. + ReLU
Reshape + Concat
FC + ReLU + Drop(0.2)
FC + ReLU + Drop(0.2)
FC
Output

1
2
3
4
5
6
7
8
9
10

Kernel
3×3
3×3
3×3
3×3
3×3
3×3
-

Dimensionality
32×32×21
32×32×32
16×16×32
16×16×64
8×8×64
8×8×128
4×4×128
130
512
512
512
P

Table 6: Network architecture of a single stream for the
proposed PosePrior network. Except for input and output
every row of the table gives a data tensor of the network
and the operations that produced it. Reduction in the spatial dimension is due to stride in the convolutions. P is the
number of estimated parameters and is P = 3 for Viewpoint estimation and P = 63 for the coordinate estimation
stream.

id
1
2
3

Name
Input crel
FC + ReLU + Dropout(0.2)
FC + ReLU + Dropout(0.2)
FC

Dimensionality
63
512
512
35

Table 7: Network architecture of the GestureNet used for
our experiments. All layers were initialized randomly.
Probability to drop a neuron in the indicated layers is set
to 0.2.

D. GestureNet architecture and learning
schedule
We train the GestureNet using Adam solver, a batch size
of 8 and an initial learning rate of 1 · 10−4 which drops
by one decade at 15000 and 20000 iterations. Training is
finished at iteration 30000. The network is trained with a
standard softmax cross-entropy loss on randomly cropped
256×256 images.

E. Additional results
Figure 10 shows results of the proposed approach.

F. NYU Hand Pose Dataset
A commonly used benchmark for 3D hand pose estimation is the NYU Hand Pose Dataset by Tompson et al. [22].

Figure 10: Qualitative examples of our complete system. Input to the network are color image and information if its a left
or right hand. The network estimates the hand segmentation mask, localizes keypoints in 2D (shown overlayed with the
input image) and outputs the most likely 3D pose. The top row shows samples from a dataset we recorded for qualitative
evaluation, the following three rows are from R-val and last three rows are from S-val.

Figure 11: Two samples from the NYU Hand Pose Dataset
by Tompson et al. [22]. Due to artefacts in the color images
this dataset is not suited to evaluate color based approaches.
We can’t use it for our work, because it only provides registered color images, which exclusively provide color information for pixels with valid depth data. This results into
corrupted images as shown in Figure 11. This makes it infeasible to use for an approach that only utilizes color.

