IET Research Journals

Multi-view pose estimation with
mixtures-of-parts and adaptive viewpoint
selection

ISSN 1751-8644
doi: 0000000000
www.ietdl.org

Emre Dogan1,2,3∗ Gonen Eren3 Christian Wolf1,2 Eric Lombardi1 Atilla Baskurt1,2
1

Université de Lyon, CNRS
INSA-Lyon, LIRIS, UMR CNRS 5205, F-69621, France
3
Galatasaray University, Dept. of Comp. Eng., 36 Ciragan Cd, Istanbul 34349, Turkey
* E-mail: edogan@gsu.edu.tr

arXiv:1709.08527v1 [] 25 Sep 2017

2

Abstract: We propose a new method for human pose estimation which leverages information from multiple views to impose a
strong prior on articulated pose. The novelty of the method concerns the types of coherence modelled. Consistency is maximised
over the different views through different terms modelling classical geometric information (coherence of the resulting poses) as
well as appearance information which is modelled as latent variables in the global energy function. Moreover, adequacy of each
view is assessed and their contributions are adjusted accordingly. Experiments on the HumanEva and UMPM datasets show that
the proposed method significantly decreases the estimation error compared to single-view results.

This paper is a preprint of a paper submitted to IET Computer
Vision. If accepted, the copy of record will be available at the IET
Digital Library.

1

Introduction

Human pose estimation is a building block in many industrial
applications such as human-computer interaction, motion capture
systems, etc. Whereas the problem has been almost solved for easy
instances, such as cooperative settings in close distance and depth
data without occlusions, other realistic configurations still present a
significant challenge. In particular, pose estimation from RGB input
in non-cooperative settings remains a difficult problem.
Methods range from unstructured and purely discriminative
approaches in simple tasks on depth data, which allow real-time performance on low-cost hardware, up to complex methods imposing
strong priors on pose. The latter are dominant on the more difficult
RGB data but also increasingly popular on depth. These priors are
often modelled as kinematic trees (as in the proposed method) or,
using inverse rendering as geometric parametric models (see section
2 for related works).
In this paper, we leverage the information from multiple (RGB)
views to impose a strong prior on articulated pose, targetting applications such as video surveillance from multiple cameras. Activity
recognition in this context is frequently preceded by articulated
pose estimation, which — in a non-cooperative environment such
as surveillance — can strongly depend on the optimal viewpoint.
Multi-view methods can often increase robustness w.r.t. occlusions.
In the proposed approach, kinematic trees model independent
pose priors for each individual viewpoint, and additional terms
favour consistency across views. The novelty of our contribution lies
in the fact that consistency is not only forced geometrically on the
solution, but also in the space of latent variables across views.
More precisely, a pose is modelled as mixtures of parts, each
of which is assigned to a position. As in classical kinematic trees,
deformation terms model relative positions of parts w.r.t. neighbours
in the tree. In the lines of [1], the deformations and the appearance terms depend on latent variables which switch between mixture
components. This creates a powerful and expressive model with
low-variance mixture components which are able to model precise
relationships between appearance and deformations. Intuitively, and
as an example, we could imagine relative positions of elbow and

IET Research Journals, pp. 1–8
c The Institution of Engineering and Technology 2015

forearm to depend on a latent variable, which itself depends on the
appearance of the elbow. It is easy to see that a stretched elbow
requires a different relative position than a bent elbow.
In the proposed multi-view setting, positions, as well as latent
variables, are modelled for each individual view. A global energy
term favours a consistent pose over the complete set of views, including consistency of the latent part type variables which select mixture
components. Here the premise is that appearance may certainly differ between viewpoints, but that a given pose is translated into a
subset of consistent latent mixture components which can be learned.
An overview of the proposed method can be seen in Fig. 1 which
depicts the iterative nature of the multi-view pose estimation process. Basically, one of the single-view estimations is selected as the
support pose and provides additional information to the other view.
On each iteration, support and target poses are swapped so that both
predictions improve over iterations. The optimisation loop continues
until convergence, where a final pose is produced for each view.
As a summary, our main contributions are the following:
• We propose a global graphical model over multiple views including multiple constraints: geometrical constraints and constraints over
the latent appearance space.
• We propose an iterative optimization routine.
• We introduce an adaptive viewpoint selection method, which
removes viewpoints if the expected error is too high.

2

Related Work

Human pose estimation from RGB images has received increasing
attention, we therefore restrict this section by excluding techniques
that exploit depth images and focus on part-based models, generative
and discriminative probabilistic models, tracking methods and deep
neural networks.
Pictorial structures (PS) — are a dominant family of models.
Based on the original idea in [2], they model an object as a combination of parts related to a star-shaped or tree-shaped structure and
deformation costs. The problem is formulated as an energy function
with terms for appearance similarity plus deformation terms between
parts [3, 4]. Although efficient for inference, tree-structured models
are known to suffer from the double-counting problem, especially
for limb parts. To address this issue loopy constraints are commonly

1

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.

Fig. 1: Overview of the model: a) Initial pose estimation running the single-view model on each view separately. The pose with the highest
confidence score is selected as the support pose. b) Joint estimation loop with geometrical and appearance constraints provided by support
pose. The newly obtained pose becomes the support pose at the end of each iteration and provides constraints for the other view. c) After
convergence, the last two poses are returned as the final results. Best viewed in colour.
used, but they require diverse approximate inference strategies [5–7].
The relationship between the non-adjacent body parts is discussed
in [8] where a fully-connected model is proposed. Due to midlevel representations conveyed by poselets, unary and binary terms
are updated during test time and the model is reduced to a classical PS, which can be solved directly. [9] proposes a PS-inspired
model, with binary random variables for each part, they model the
presence and absence for every position, scale and orientation. However, this results in a high number of variables which forces them to
approximate inference.
Flexible Mixtures of Parts (FMP) — have been introduced by
[1], to tackle the limited expressiveness of tree-shaped models.
Instead of orientations of parts, they proposed mixtures, which are
obtained by clustering appearance information. A detailed review of
the method can be found at section 3. Among extensions, [10] proposed appearance sharing between independent samples to cluster
similar background and foregrounds. [11] presented a method to estimate 3D pose from a single image where they use FMP and camera
parameter estimation, in conjunction with anthropomorphic constraints. [12] proposed an improvement by aggregation of multiple
hypotheses from the same view using kernel density approximation.
Multi-view settings — allow various methods to emerge, particularly for 3D pose estimation. [13] proposed a generalised version of
PS, which also exploits temporal constraints. They employ graphs
spanning multiple frames and inference is performed with nonparametric belief propagation. Among 3D extensions of PS, the
burden of the infeasible 3D search space is handled by reducing
it with discretisation [14], supervoxels [15], triangulation of corresponding body parts from different viewpoints [16] or by using
voxel-based annealing particle filtering [17]. Recently, [18] proposed
a strategy similar to 3D-PS, but with a realistic body model, and
inference is carried out with particle-based max-product belief propagation. Inferring 3D pose from multiple 2D poses is also common,
with various underlying strategies such as hierarchical shape matching [19], random forests [20] and optical flow [21]. [22] introduced
a scheme where PS is employed to estimate 2D poses, then incorporates them to obtain a 3D pose with geometrical constraints, colour
and shape cues. Although being somewhat analogous to our proposition, [22] does not consider cases where some viewpoints are more
beneficial than others, which we leverage with adaptive viewpoint
selection.
Temporal strategies — are commonly used for pose estimation
and articulated tracking from videos. Using spatiotemporal links

2

between the individual parts of consecutive frames seems promising,
but intractability issues arise. To this end, [7] opt for approximation with distance transform [23]. [6] reduce the graph by combining
symmetrical parts of human body and generating part-based tracklets
for temporal consistency. [24] uses a spatiotemporal And/Or Graph
to represent poses where only temporal links exist between parts.
Recently [25] proposed synthesising hypotheses by applying geometrical transformations to initially annotated pose and match next
frame with the nearest neighbour search.
Discriminative approaches — learn a direct mapping from feature space to pose, often by avoiding any explicit body models
(although models can be integrated). Silhouettes [26] and edges [27]
are frequently used as image features in conjunction with learning
strategies for probabilistic mapping, such as regression [26], Gaussian Processes [28] and mixtures of Bayesian Experts [5]. Previous
work shows that these approaches are usually computationally efficient and perform well in controlled environments, but they are
highly dependent on the training data and therefore tend to generalise
poorly in unconstrained settings.
Deep neural networks — have received remarkable attention
recently which inevitably affected the pose estimation challenge.
[29] address the problem by first obtaining 2D pose candidates with
PS, then utilising a deep model the determine the final pose. [30]
feeds both local patches and their holistic views into the Convolutional Neural Networks (CNN), while [31] proposes a new architecture where deep CNNs are used in conjunction with Markov Random
Fields. [32] on the other hand, follow a more direct approach and
employ a cascade of Deep Neural Network regressors to handle the
pose estimation task. [33] uses a kinematic tree, where the same deep
network learns unary terms as well as data dependent binary terms.
Similar to our adaptive viewpoint selection scheme, predicting the
estimation error during test time is explored by [34]; however they
employ a CNN to learn iterative corrections that converge towards
the final estimation. Part mixtures is adopted in [35, 36], where
message passing is implemented as additional layers. State-of-theart results on single-view benchmarks are achieved by [37], where
multiple hourglass layer modules are stacked end-to-end.
Our method is based on FMP, which allows imposing a strong
prior on pose, generalising it to multiple views. Compared to existing multi-view approaches, our method is not restricted to geometric
coherence terms. We enforce coherence also in the space of latent
variables (the mixture component selectors), which further improves

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.
of views to two and consider a setup with calibrated cameras. In this
case, a global energy function models the pose quality over a pair
views A and B, taking as input images I A and I B and estimating
pose variables pA and pB while additionally optimising over latent
part types tA and tB :

(a)

(b)

(c)

Fig. 2: Illustration of the learned multi-view consistency term over
latent appearance (part types). The picture shows HoG filters of the
same part (the shoulder). Filter pair (a – b) has been learned to be
highly compatible (eventually across different viewpoints), whereas
compatibility of pair (a – c) has been learned to be low, according to
training data.
performance. Additionally, we leverage the consistency between
views by predicting the fitness of each view during test time.

3

Single view pose estimation

In the lines of [1], an articulated pose in a single 2D image is modelled as a flexible mixture of parts (FMP). Related to deformable
part models introduced by [38], part-based models for articulated
pose estimation are classically tree structured. Similarly, our model
is a kinematic tree on which a global energy is defined including
data attached unary terms and pairwise terms acting as a prior on
body pose. The underlying graph is written as G = (V, E), where
vertices are parts and edges are defined on adjacency between parts.
Let pi = (x, y) be the pixel coordinates for part i ∈ {1, . . . , K}
in image I. The values of pi are the desired result values over
which we would like to optimise. Additional latent variables ti ∈
{1, . . . , T } model a type of this part, which allows to model terms
in the energy function for given types, effectively creating a powerful mixture model. In practice, the part types are set to clusters of
appearance features during training time. In the single-view version,
the energy function corresponds to the one given in [1]. Defined over
a full pose p = {pi }, input image I and latent variables t = {ti }, it
is given as follows:
P
t ,t
witi φ(I, pi ) + ij∈E wiji j ψ(pi − pj )
P
P
t ,t
+ i∈V btii + ij∈E biji j
(1)
The expression in the first sum corresponds to data attached terms,
where φ(I, pi ) are appearance features extracted at pi (HoG, see
section 7). Note that the corresponding trained parameters witi
depend on the latent part type ti .
The pairwise terms in the next expression model the prior over
body pose using a classical second degree deformation ψ(pi −
pj ) = [dx dx2 dy dy 2 ]T where dx = xi −xj and dy = yi −yj .
They control the relative positions of the parts and act as a “switching” spring model, the switching controlled by the latent part types
ti .
The last two sums define a prior over part types including unary
t ,t
part type biases btii and pairwise part type terms biji j .
Although scale information is not specified in the equations, a
pyramid is used in the implementation to address the various sizes
of the subjects in the image. Inference in this model (and in our
generalisation to multi-view problems) will be addressed in section
6.
S(I, p, t)

4

=

P

i∈V

Multi-view pose estimation

We generalise the single-view model to multiple views and show
that geometrical consistency constraints can be leveraged to improve
estimation quality. Without loss of generality let us fix the number

S(I A , I B , pA , pB , tA , tB )

=

S(I A , pA , tA )
P
B
+α i∈V ai ξ(pA
i , pi )

+ S(I B , pB , tB )
P
B
+ β i∈V ai λ(tA
i , ti )

(2)

Here, S is the single pose energy from equation 1. The two additional terms ξ and λ ensure consistency of the pose over the two
views, α and β are the hyperparameters that controls the amount of
influence of these terms, and ai are binary variables activating or
deactivating consistency. All terms and symbols are described in the
corresponding sections below.
4.1

Geometric constraints

Assuming temporal synchronisation, images I A and I B show the
same articulated pose from two different viewpoints, which can be
exploited. In particular, given calibrated cameras, points in a given
view correspond to epipolar lines in the second view. The geometric
term ξ leverages this as follows:
B
A
B
B
A
ξ(pA
i , pi ) = −d(pi , e(A, pi )) − d(pi , e(B, pi ))

(3)

where e(A, pB
i ) is the epipolar line in view A of point pi in view
B and d is the Euclidean squared distance between a point and a
line. Thus, geometric constraints translate as additional energy to
particular locations for both views in the global energy function.
4.2

Appearance constraints

The geometric constraints above are imposed on the solution (positions pi ). The term λ adds additional constraints on the latent part
type variables ti , which further pushes the result to consistent solutions. Recall that the latent variables are clusters in feature space,
i.e. they are related to types of appearance. Appearances might, of
course, be different over views as a result of the deformation due
to viewpoint changes. However, some changes in appearances will
likely be due to the viewpoint change, whereas others will not. Intuitively, we can give the example of an open hand in view A, which
will certainly have a different appearance in view B; however, the
image will not likely be the one of a closed hand.
We model these constraints in a non-parametric way as a discrete
B
A B
distribution learned from training data, i.e. λ(tA
i , ti ) = p(ti , ti )
(see section 5). Figure 2 illustrates this term using three filter examples shown for the learned model of part right shoulder. The λ term
is high between (2a) and (2b), but low between (2a) and (2c). Intuitively, (2a) and (2b) look like the same 3D object seen from different
angles, whereas (2a) and (2c) do not.
4.3

Adaptive viewpoint selection

Geometric and appearance constraints rely on the accuracy of the
initial single-view pose estimates. In certain cases, the multi-view
scheme can propagate poorly estimated part positions over views,
eventually deteriorating the multi-view result. To solve this problem, we would like to estimate beforehand, whether an additional
view can contribute, i.e. increase performance, or whether it will
deteriorate good estimations from a better view.
We propose an adaptive viewpoint selection mechanism and introduce a binary indicator vector (over parts) that switches on and off
geometric and appearance constraints for each part during inference.
If an indicator is switched off for a part, then the support pose does
not have an effect on the optimised pose for this part. The binary

3

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.
Table 1 HumanEva – PCP3D scores (%) of our model trained

indicator vector a is given as follows:

ai =

0
1

A

on subject 1, evaluated on subject 1 and all subjects combined,
with PCP threshold 0.5. Performance of is compared to Flexible
Mixture of Parts (FMP) [1] method.

B

if σi (p , θ) > τi or σi (p , θ) > τi
else

(4)

where τi is a threshold obtained from median part errors on the training set and σi (pA , θ) is a function with parameters θ that estimates
the expected error committed by the single-view method for part i,
given an initial estimate of the full pose pA .
σ is a mapping learned as a deep CNN taking image tiles cropped
around the initial (single-view) detection pA as input. Training the
network requires to minimise a loss over part estimation errors, i.e.
an error over errors, as follows:
min || σ(pA , θ) − e ||2
θ

FMP [1]

———— Ours ————
Geom. +App. +Adap.

S1
All
S1
All
S1
All
S1
All

Box
Box
Gestures
Gestures
Jog
Jog
Walking
Walking

77.34
67.14
78.91
74.68
84.91
77.52
84.65
78.49

82.70
69.45
84.27
77.38
86.75
80.16
86.71
81.69

83.87
70.23
84.08
78.81
86.70
79.84
86.50
81.96

85.31
71.57
88.14
80.34
86.86
80.97
87.68
83.17

S1
All

Overall
Overall

82.02
74.86

85.43
77.62

85.49
78.11

87.24
79.40

Table 2 UMPM – PCP3D scores (%) on all
sequences with PCP threshold 0.5, compared to Flexible Mixture of Parts (FMP) [1] method.
Sequence

FMP [1]

———— Ours ————
Geom. +App. +Adap.

Chair
Grab
Orthosyn
Table
Triangle

74.72
74.23
72.47
70.30
73.69

78.09
76.25
74.65
73.49
77.26

77.54
77.18
75.22
74.18
77.81

79.94
81.92
76.48
77.86
83.81

Overall

73.07

75.91

76.37

80.04

Training

Single-view parameters: Appearance coefficients witi , deformation
t ,t
t ,t
coefficients wiji j and part type prior coefficients btii and biji j are
learned as in [1]: we proceed by supervised training with positive and
negative samples, where the optimisation of the objective function is
formulated as a structural SVM. Part type coefficients are learned
w.r.t. their relative positions to their parents by clustering. This mixture of parts approach ensures the diversity of appearances of part
types where their appearance is associated with their placement with
reference to their parents; for example a left-oriented hand is usually
seen on the left side of an elbow, while a upward facing hand is likely
to occur above an elbow.
B
Consistency parameters: The discrete distribution λ(tA
i , ti ) =
A B
p(ti , ti ) related to the appearance constraints between views is
learned from training data as co-occurrences of part types between
the viewpoint combinations. We propose a weakly-supervised training algorithm which supposes annotations of the pose (positions pi )
only, and which does not require ground truth of part types ti . In
particular, the single-view problem is solved on the images of two
different viewpoints and the resulting poses are checked against the
ground truth poses. If the error is small enough, the inferred latent
B
variables ti are used for learning. The distribution p(tA
i , ti ) is thus
A
estimated by histogramming eligible values for ti and tB
i . Fig 2
shows an example of learned filters and their compatibility.
The hyper-parameters α and β weighting the importance of the
consistency prior are learned through cross-validation over a holdout set (see section 7).

Viewpoint selection parameters: As seen in Section 4.3, σ is a mapping that estimates error of a single-view pose estimation, given an
image tile cropped around the bounding box. To determine σ, we
use regression of the expected error and train a deep CNN. We use
a VGG-16 network [39] pre-trained on ImageNet and remove all the
top fully connected layers and replace them with a single small hidden layer for regression. We finetune the last convolutional block
of VGG and learn the weights of the newly added fully connected
layers with augmented data (see section 7 for further details).

4

Sequence

(5)

where e is the vector of ground truth errors obtained for the different
parts by the single-view method, and || · ||2 is the L2 norm which is
here taken over a vector holding estimations for individual parts. θ
are the parameters of the deep network.
We argue that such a network is suitable to anticipate whether
an individual part is useful for multi-view scheme, by implicitly
learning multi-level features from an image tile. For example, selfoccluded parts or other poor conditions would most likely to be
associated with high error rates, whereas unobstructed views would
yield low errors. Thresholding the output of the network, namely the
error estimations σi , can provide the decision whether the support
view has an influence for part i or not.

5

Subject

6

Inference

Inference of the optimal pose pair requires maximising equation (2)
B
over both poses pA
i and pi and over the full set of latent variables
B
tA
and
t
.
Tractability
depends
on the structure of the graph, and
i
i
on the clique functionals. Whereas the graph G = (V, E) for the
single-view problem (the graph underlying equation 1) is a tree,
the graph of the multi-view problem contains cycles. This can be
seen easily, as it is constructed as a union of two identical trees with
additional edges between corresponding nodes, which are due to the
consistency terms. Compared to the single-view problem, maximisation cannot be carried out exactly and efficiently with dynamic
programming.
Several strategies are possible to maximise equation (2): approximative message passing (loopy belief propagation) is applicable
for instance, which jointly optimises the full set of variables in an
approximative way, starting from an initialisation. We instead chose
an iterative scheme which calculates the exact solution for a subset
of variables keeping the other variables fixed, and then alternates.
In particular, as shown in figure 1, we optimise for a given view
while keeping the variables of the other view (the “support view”)
fixed. Removing an entire view from the optimisation space ensures
that the graph over the remaining variables is restricted to a tree,
which allows solving the sub-problem efficiently using dynamic
programming.
Let us write kids(i) for the child nodes of part i. The score of a
part location pi for a given part type ti is computed as follows:

tA

B A B
ti
A A
i
scorei (tA
i , ti , pi , pi ) = bi + wi · φ(I , pi )
tB

+ bii + witi · φ(I B , pB
i )
B
A B
+ ai (αξ(pA
i , pi ) + βλ(ti , ti ))
X
B A B
+
mk (tA
i , ti , pi , pi )
k∈kids(i)

(6)

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.

Fig. 3: Illustration of the iterative optimisation process. The first and last rows are two respective viewpoints, the middle row shows epipolar
lines overlaid over the respective viewpoint. Diagonal arrows show the pose that the epipolar lines are based on. Each column is an iteration
and vertical arrows shows the resulting pose and epipolar lines used in joint estimation. Final poses are marked with green borders. Best viewed
in colour.
Table 3 PCP3D scores (%) for all limb parts with PCP threshold 0.5, compared to FMP[1] on HumanEva and
UMPM datasets.(U-L: upper left, U-R: upper right, L-L: lower left, L-R: lower right)
Configuration

U-R Arm

U-L Arm

L-R Arm

L-L Arm

U-R Leg

U-L Leg

L-R Leg

L-L Leg

FMP[1] on HumanEva
Ours on HumanEva

88.4
94.5

83.4
88.3

51.8
76.1

61.4
71.5

100
100

100
100

73.6
82.7

67.9
73.6

FMP[1] on UMPM
Ours on UMPM

50.6
69.8

50.7
63.6

31.3
45.2

28.6
35.6

99.4
99.6

98.6
99.5

78.4
84.4

64.6
75.1

with the message that part i passes to its parent j is defined as:
h tA ,tA
tB ,tB
B A B
i
j
mi (tA
+ biji j
j , tj , pj , pj ) = max bij
B
tA
i ,ti

B A B
+ max scorei (tA
i , ti , pi , pi )
B
pA
i ,pi

tA ,tA
j

+ wiji

tB ,tB
j

A
i
· ψ(pA
i , pj ) + wij

B
· ψ(pB
i , pj )
(7)

As mentioned, one of the two sets A and B is kept constant at each
time, which simplifies the equations (6, 7) to a single-view form,
similar to [1]. Messages from all children of part i are collected
and summed with the bias term and filter response, resulting in the
score for that pixel position and mixture pair. As classically done in
deformable parts based models, the optimisation can be carried out
with dynamic programming and the inner maximisation in equation
(7) with min-convolutions (a distance transform, see [23]).
The algorithm is initialised by solving the single-view problem
independently for each viewpoint. The pose with the lowest estimated error (see section 4.3) is chosen as initial support pose, the
pose of the other viewpoint being optimised in the first iteration.
The iterative process is repeated on until convergence or a maximum number of iterations is reached. Optimising each sub-problem
is classical, where the message passing scheme iterates from the
leaf nodes to the root node. After thresholding to eliminate weak
candidates and non-maximum suppression to discard similar ones,
backtracking obtains the final pose.

7

Experiments

We evaluated our work on two datasets, HumanEva I [40] and
Utrecht Multi-Person Motion (UMPM) [41]. Both datasets have been
shot using several calibrated cameras. Ground truth joint locations
were recorded with a motion capture system, with 20 and 15 joints,
respectively.

i

For HumanEva set we only use three cameras (C1, C2 and C3),
which are placed with 90 degrees offset. Three subjects (S1, S2
and S3) perform following activities: walking, boxing, jogging, gestures and throw-catch. There are three takes for each sequence, used
for training, validation and test. Since the creators of HumanEva
favour online evaluation, original test set does not contain ground
truth joint positions. Following [22], we divided the original training
set into training and validation sets and used the original validation
set for testing. All hyper-parameters have been optimised over our
validation set.
For UMPM set, all available cameras (F, L, R and S) were
used. We considered all available sequences with one subject, which
includes object interactions such as sitting on a chair, picking up a
small object, leaning and lying on a table. The training, validation
and test partitions were divided using 60%, 20% and 20% of the
all available data, respectively. The HumanEva test set consists of
4493 images per camera, while UMPM test set has 6074 images per
camera. The number of distinct images used in the tests sums up to
13479 and 24296, respectively.
Since our model is trained with 26 parts, we used a linear function
to convert our box centres to the 20 joint locations for HumanEva
and 15 joints locations for UMPM.
The data attached terms φ(., .) in this work were based on HoG
features from [42]. Other features are possible, in particular learned
deep feature extractors as in [33] or [43, 44]. This does not change
the setup, and can be performed with finetuning of a pre-trained
model for this case, where the amount of training data is relatively
low.
We evaluate our multi-view approach against the single-view
method given in [1]. We use two poses as input and evaluate on one
of these two poses, varying over multiple configurations.
Parameters of the single-view model (Eq. 1) are learned on all
activities of S1 for HumanEva. We took 100 frames with equal time
intervals for every activity from three cameras for training, which
sums up to 1500 images. The remainder of the data was set as the
validation set. For UMPM, nearly 400 consecutive frames for each
sequence were used as positive samples. As for the negative samples,

5

AR
M

AR
TO M
RS
O
HI
U- P S
R
LE
G
UL
LE
G
LR
LE
G
LL
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G

LL

LR

UL

-R

H

(a) HumanEva, S1, all sequences

LL

0.0

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LR

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-R

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H

0.6

AR
TO M
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U- P S
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1.0

EA
SH
D
OU
LD
ER
S
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1.0

EA
SH
D
OU
LD
ER
S
U

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.

(b) UMPM, all sequences

Fig. 4: PCP3D scores (%) for individual parts obtained by FMP[1] (red) and ours (green) on both datasets. (U-L: upper left, U-R: upper right,
L-L: lower left, L-R: lower right)

Fig. 5: Qualitative comparison of all three subjects performing various activities from different viewpoints. Top: poses obtained with the
single-view model [1]. Bottom: poses obtained with multi-view pose estimation. Best viewed in colour.

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.0
0.0
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

(a) HumanEva, S1, all sequences

(b) UMPM, all sequences

Fig. 6: PCP3D curves (%) obtained by FMP[1] (red) and ours
(green) on both datasets, as a function of PCP threshold γ, which
controls the ratio of the detected part segments to the ground truth to
be considered correctly detected, as defined in Eq. (8).

background images from corresponding datasets were used in addition to the INRIA Person Database [42]. Hyper-parameters α and β
of equation (2) were learned on validation sets.
To learn the weights of the error estimating CNN σi (·), training
data sets were augmented with horizontal flip, Gaussian blur and
additive noise. As mentioned earlier, we used a finetuned version
of VGG-16 [39] model using pre-trained weights on ImageNet to
estimate the part-based error of the single-view pose. We removed

6

the fully connected layers and introduced our top model with a hidden layer of 1024 nodes, an output layer of K nodes and parametric
ReLU as non-linearity. First, weights of the complete VGG-16 network were frozen so that they are unaffected by the backpropagation
and weights of the top model were roughly learnt with a high learning rate. Then, the top model were initialised with these weights,
and the last convolutional blocks (namely the last three conv3-512
layers) were unfrozen for finetuning. We preferred stochastic gradient descent as optimisation algorithm with small learning rate to
ensure that the weights of the last convolutional block are marginally
updated. To prevent overfit to augmented data sets we applied strong
regularisation and also employed Dropout [45] with a probability of
0.5.
For each multi-view arrangement, i.e. pair combinations of cameras, two pose estimations are produced. Since each view belongs
to several multi-view arrangements, we end up with several pose
candidates for the same viewpoint, e.g. we obtain two pose candidate for C1, once from the C1-C2 pair and once from the C3-C1
pair. These candidates are simply averaged and obtained 2D poses
are triangulated non-linearly to obtain 3D pose for a single time
frame. Following the literature on 3D pose estimation [14, 15] we
use the percentage of correctly detected parts in 3D (PCP3D), which
is calculated as

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.
Table 4 Comparison of our performance to SHN [37] on sub-

kŝn − sn k + kên − en k
≤ γkŝn − ên k
2

ject 1 of HumanEva dataset, in terms of PCP3D score (%). First
reported result is calculated with unrestricted images (i.e. same
input for our method) which is dubbed as Standard, while the
second one is calculated with cropped input images around
the person with a scale requirement, which is dubbed as Preprocessed. (U-L: upper left, U-R: upper right, L-L: lower left, L-R:
lower right)

(8)

where sn and en are the estimated start and end 3D coordinates of
the n’th part segment, and ŝn and ên are the ground truth 3D coordinates for the same part segment. By convention we take γ = 0.5
in all our computations, unless specified otherwise.
Performances — are shown in Table 1 as PCP3D scores on train
subject S1 only and over all subjects; while table 2 shows PCP3D
scores on UMPM test set. We provide three versions: geometric constraints only, geometric and appearance constraints combined, and
both constraints with adaptive viewpoint selection. It is clear that
in all cases and both data sets, the multi-view scheme significantly
improves performance. Depending on the performed action, gains
can be significant up to 9.2% in HumanEva and 10.1% in UMPM.
The last columns of tables 1 and 2 show that the additional coherence terms decrease the error. Fig. 4 demonstrates that this error is
distributed over all different parts of the body: we improve most on
wrists and elbows, which are important joints for gesture and activity
recognition, as seen in table 3. Plots for overall PCP3D curves w.r.t.
various thresholds are also given in Fig. 6.
The adaptive viewpoint selection — effectively prevents erroneous consistency terms for certain parts dynamically, due to poor
initial single-view estimations as discussed in Section 4.3, and
shown in table 1.
Fig. 3 depicts intermediate poses and epipolar lines throughout
the course of algorithm while Fig. 5 shows several examples from
the test set, where faulty poses are corrected with the multi-view
approach. Note that limbs are in particular subject to correction by
geometrical and appearance based constraints, since they are considerably susceptible to be mistaken for their respective counterpart. It
should be also noted that in case of poor initial detections, a faulty
part location can be propagated through the constraints and deteriorate a correct part estimation in other views. Performance tables
show that our adaptive viewpoint selection scheme successfully prevents this by considerably decreasing the number of deterioration
cases. Particularly, Fig. 7 depicts the the amount of improvements
and deteriorations w.r.t the baseline, with and without the adaptive
viewpoint selection scheme, which efficiently discards the erroneous
single-view part detections.
Comparison to the state of the art — We compare to the original FMP [1], to Schick et al.’s voxel carving based 3D PS method
[15] and to pre-trained Stacked Hourglass Networks (SHN) [37].
[15] report 78% PCP3D for HumanEva and for all sequences of
S1 and S2 (ours: 83.42%) and they report 75% for UMPM and for
all sequences of P1 (ours: 80.04%).
SHN [37] requires a cropped input image that is centred around
the person with specific scale requirements. Similar to our scheme,
2D poses from different views were triangulated to obtain 3D pose.
Table 4 depicts our estimation performance and two versions of
[37]: First one is with unrestricted images, i.e. same input to our
method; and second one with pre-processing steps that require the
ground truth. Please note that the SHN heavily depends on the preprocessing, and fails if the person is not centred on image. Our
method, which does not require such supervision, obtains similar or
better performance to SHN with pre-processed input.

Fast parallel implementation: Our implementation is based on our
port of the Matlab/C++code from the single-view method by [1]
to 100% pure C++, where crucial parts have also been ported to
GPU processing using NVIDIA’s CUDA library. This sped up runtime from 3000ms/frame to 880ms/frame on a computer equipped
with a 2.4Ghz Xeon E5-2609 processor and an NVIDIA 780 Ti
GPU for the single-view algorithm (for a 172x224 image with 32
levels of down-sampling). The multi-view algorithm is slower as
5.73 iterations are performed in average before the results are stable.
We are currently working on additional optimisations of computational complexity using approximative parallel implementations of
the distance transform on GPUs.

Body Part

Ours

SHN Standard

SHN Pre-processed

Head
Shoulders
U-R Arm
U-L Arm
L-R Arm
L-L Arm
Torso
Hips
U-R Leg
U-L Leg
L-R Leg
L-L Leg

100.0
99.47
94.52
88.31
76.09
71.53
100.0
60.63
100.0
100.0
82.69
73.58

7.40
49.08
15.13
45.51
7.73
41.88
52.58
0.0
52.64
51.65
51.65
48.15

25.17
100.0
96.37
98.48
73.98
84.54
100.0
65.52
100.0
100.0
99.41
98.15

Overall

87.24

35.28

86.80

(a) Without adaptive viewpoint selection

(b) With adaptive viewpoint selection

Fig. 7: Illustration of the effect of adaptive viewpoint selection. The
histograms show differences of errors (px) compared to the baseline [1]. Negative differences (red) indicate that our method performs
worse, positive differences (green) indicate that our method yielded
a better pose. The adaptive mechanism reduces deteriorations while
keeping improvements.

8

Conclusion

We proposed a novel multi-view method to estimate articulated
body pose from RGB images. Experiments show that combining
appearance constraints with geometrical constraints and adaptively
applying them on individual parts yields better results than the
original single-view model. We also show that our algorithm performs more accurately regardless of the view combinations, and it
generalises well in a way to handle unseen subjects and activities.

7

This paper is a preprint of a paper submitted to IET Computer Vision. If accepted, the copy of record will be available at the IET Digital
Library.
We plan to extend and evaluate our method in settings with three
or more viewpoints, which should be straightforward. Generally, a
graph modelling the possible interactions could possibly have highorder cliques, where each clique contains nodes corresponding to
the possible views. In practice, it is unsure whether high-order interactions should provide more powerful constraints then (sub)-sets of
pairwise constraints. A straightforward algorithm should be similar to the one proposed in the paper: optimizations are carried out
over a single-view including pairwise terms involving the different
(multiple) support views.
Another improvement would be the extension to a non-calibrated
setting, by exploring the self-calibration and epipolar line estimation
techniques, which would allow our method to be used in multi-agent
robotic systems.

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Acknowledgment

This work was partly supported by a grant of type BQR by Fondation
INSA-Lyon, project CROME.

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