Dual Path Networks for Multi-Person Human Pose Estimation
Guanghan Ning, Zhihai He
University of Missouri
Columbia, MO

arXiv:1710.10192v1 [] 27 Oct 2017

gnxr9@mail.missouri.edu, hezhi@missouri.edu

Abstract

60% in mean average precision (mAP).
Existing methods can be classified into two kinds of approaches, the top-down approach and the bottom-up approach. The top-down approach [11, 22] relies on a detection module to obtain human candidates and then apply a
single-person human pose estimator to detect human keypoints. The bottom-up approach [8, 12, 34, 20], on the
other hand, detects human keypoints from all potential human candidates and then assemble these keypoints into human limbs for each individual based on various data association techniques. The main advantage of the bottom-up
approaches is its excellent tradeoff between estimation accuracy and computational cost. It takes the winner [8] of
MS COCO 2016 keypoint challenge less than 200 ms to
run the pose estimator for one frame on a Pascal TITAN X
GPU. More importantly, contrary to top-down approaches,
its computational cost is invariant to the number of human
candidates in the image. We follow the bottom-up approach
of the works of Zhe. et. al [8] and aim to design smaller,
faster, and more accurate neural networks for multi-person
keypoints regression. According to [8], their proposed Part
Affinity Fields (PAF) and its corresponding data-association
techniques are robust and reliable. More accurate keypoint
and PAF regression would potentially increase the overall
performance up to 10%.
In this work, we focus on the network regression part
and leave the data association part to future works. We
propose a dual-path network specially designed for multiperson human pose estimation, and compare our performance with the openpose [2] network in aspects of model
size, forward speed, and estimation accuracy. Our contributions include: (1) We analyze the tasks of keypoint regression and PAF, where PAF estimation depends heavily
on keypoints estimation but not vice versa. (2) We then design a dual-path network, the denseNet path responsible for
PAF regression while the resNeXt path regressing human
keypoints. Our performance is superior than the openpose
[2] network even though the proposed network is of lower
computational complexity and smaller model size.
The rest of the paper is organized as follows. In section

The task of multi-person human pose estimation in natural scenes is quite challenging. Existing methods include
both top-down and bottom-up approaches. The main advantage of bottom-up methods is its excellent tradeoff between estimation accuracy and computational cost. We follow this path and aim to design smaller, faster, and more accurate neural networks for the regression of keypoints and
limb association vectors. These two regression tasks are
naturally dependent on each other. In this work, we propose
a dual-path network[9] specially designed for multi-person
human pose estimation, and compare our performance with
the openpose[2, 8] network in aspects of model size, forward speed, and estimation accuracy.

1. Introduction
The task of human pose estimation is to determine the
precise pixel locations of body keypoints from a single input
image [15, 21]. Human pose estimation is very important
for many high-level computer vision tasks, including action
and activity recognition, human-computer interaction, motion capture, and animation. Estimating human poses from
natural images is quite challenging. An effective pose estimation system must be able to handle large pose variations, changes in clothing and lighting conditions, severe
body deformations, heavy body occlusions [32, 31, 21]. It
is naturally a regression task. With Convolutional Neural
Networks (ConvNets) and many assistive methods such as
batch normalization [16], resnet [13], and inception design
[28, 29], single-person human pose estimation has recently
achieved significant progress.
Recent research emphasis has been put on multi-person
human pose estimation, where multiple individuals may exist in a natural scene. Compared to single person human
pose estimation, where human candidates are cropped and
centered in the image patch, the task of multi-person human
pose estimation is more challenging. The best performance
on MS COCO 2016 Keypoints challenge [1] is only around
1

Channels = 3
Channels = Y
n end-to-end DPN blocks
<M, M>
<X+Z, Y>
VGG-16

...

<X+(n-1)Z, Y>
<X+nZ, Y>

Channels= M

<X>
k=1x1
<128>

<128>
k=1x1
<kpts>

<X+nZ>
k=1x1
<128>

<128>
k=1x1
<PAFs>

Channels = X+nZ

t stages

<185>
k=1x1
<N>
Channels = N

F
Channels = 128
Convolution

DPN block

Batch Norm

Relu Unit

Concatenate

Figure 1. Our proposed network. The image feeds into the first 16 layers of VGG [26] network and outputs 128 channels of low-level
visual features. These features are subsequently fed into each stage of the repetive sub-network illustrated in this image. Each DPN block
takes as input two branches of feature maps and also output feature maps of two branches, one with a consistent number of channels, the
other one with accumulated channels over DPN blocks.

2, we provide a brief review of recent works on multi-person
human pose estimation. Section 3 introduces the proposed
network. Section 4 presents our experimental results. Section 5 concludes our paper.

2. Related Work
2.1. Single-Person Human Pose Estimation
This task is simpler than multi-person pose estimation
because it aims to estimate the pose of a single person,
where the image is cropped assuming the person dominates
the image content. Traditional methods for single-person
human pose estimation are mostly based on pictorial structure models [25, 23, 27, 30, 10, 19]. Since the work of
DeepPose by Toshev et al. [32], research on human pose
estimation has shifted from traditional approaches to deep
neural networks (DNN) due to their superior performance.
Recent methods [21, 33, 15, 7] have achieved quite accurate
performance on popular datasets [6, 18]. However, the assumption that the person can always be correctly located is
not necessarily satisfied.

2.2. Multi-Person Human Pose Estimation
Multi-person human pose estimation is a more realistic
problem. It attempts to estimate the poses of multiple persons in natural scenes. It is quite challenging due to the
variance of sizes and scales of the persons. Existing methods can be classified into two kinds of approaches, the topdown approach and the bottom-up approach.
The top-down approach [11, 22] relies on a detection
module to obtain human candidates and then apply a singleperson human pose estimator to detect human keypoints.
Insafutdinov et al [15] propose a pipeline which uses the
Faster R-CNN [24] as detection module and a unary DeeperCut as their single-person pose estimator. Their method

achieves 51.0 in mAP on MPII dataset [6]. Because the
single-person pose estimator is usually sensitive to the detection results, this approach requires the detection module
to be very robust. More accurate performance has been
achieved by Hao et al [11]. Their framework facilitates
pose estimation in the presence of inaccurate human bounding boxes by introducing more components into the pipeline
that refine the detection and pose estimation results.
The bottom-up approach [8, 12, 34, 20], on the other
hand, detects human keypoints from all potential human
candidates and then assemble these keypoints into human
limbs for each individual based on various data association techniques. Many of these techniques are graph-based
[8, 34]. The main advantage of the bottom-up approaches is
its excellent tradeoff between estimation accuracy and computational cost. The winner of COCO2016 [1] proposes
to estimate human keypoints as well as Part-Affinity Fields
(PAF) simultaneously. PAFs are limb association vectors
that can be used to assemble the keypoints into multi-person
poses with certain graph-based association techniques. According to [8], their proposed PAF and corresponding dataassociation techniques are robust and reliable. More accurate keypoint and PAF regression would potentially increase the overall performance up to 10%. We follow their
works and focus on the network regression part, aiming to
design smaller, faster, and more accurate neural networks
for multi-person human pose estimation.

2.3. Dual Path Networks
According to [8], their proposed data-association technique is robust and reliable; more accurate keypoint and
PAF regression would potentially increase the overall performance up to 10%. Motivated by this, we look into
network engineering and explore more robust and efficient
learning of features and spatial inter-dependencies.

<X>
k=1x1
<X>

Association Path

Channels = X+Z
Channels = X

Channels = Y

<X>
k=1x1
<128>

<128>
kxk
group=4
<128>

<Y>
k=1x1
<128>

<128>
k=1x1
<Y+Z>

Channels = Z
Channels = Y

<Y>
k=1x1
<Y>

Keypoints Path

Channels = Y
Convolution

Batch Norm

Relu Unit

Concatenate

Pixel-wise Add

Figure 2. Proposed DPN block. It consists of two paths: the keypoints path and the data association path. The regression of human
keypoints and association vectors are dependent on each other and share information from the previous block. The association vectors
however, need more features to further explore spatial interdependency and they are regressed with accumulated channels of feature maps
from all previous DPN blocks.

Dual Path Networks (DPN) is first proposed in [9] as
a hybrid network design that incorporates the core idea of
DenseNet [14] with that of ResNeXt [35]. ResNeXt is a
variant of the widely-used ResNet [13], introducing a homogeneous, multi-branch architecture that has a new dimension called cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions
of depth and width. They show that increasing cardinality is
able to improve classification accuracy, and is more effective than going deeper or wider when we increase the capacity of the network. The core of denseNet is that it connects
each layer to every other layer in a feed-forward fashion.
They alleviate the vanishing-gradient problem, strengthen
feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
According to the research of DPN, ResNet and its variants enable feature re-usage while DenseNet enables new
features exploration which are both important for learning good representations. By carefully incorporating these
two network designs into dual-path topologies, DPN shares
common features while maintaining the flexibility to explore new features through dual path architectures.
Inspired by the DPN network that is originally designed
for the task of image classfication, we aim to design a variant of DPN that is specially tailored for multi-person human
pose estimation because the regression tasks for keypoints
and association vectors are naturally two paths. The two
tasks are dependent on each other but unique in their own
ways. In the next section, we introduce our proposed DPN
network, describe how the regression of keypoints and association vectors are assigned to each path and, explain the
intuition behind it. For detailed description of part associ-

ation techniques, please refer to the original paper of PAF
[8].

3. Proposed Method
The proposed network is highly modulized. We intentionally follow the general network structure of openpose.
As shown in Figure 1, there are multiple stages of repetitive subnetworks, where each subnetwork outputs estimated
heatmaps of keypoints and PAFs and is enforced with loss
functions as intermediate supervision. The network is first
fed with an image into the first 16 layers of the VGG network and outputs 128 channels of low-level visual features,
denoted by F. These features are then fed into each stage of
the following subnetworks. The modules in the figure only
indicates the input and output channels and leaves out the
resolution because the convolutional layers are all padded
such that the resolution of the feature maps do not change.
Our proposed network differs from the openpose network in the structure of the subnetwork patterns, specifically, the DPN blocks. As shown in Figure 2, our proposed DPN block consists of two paths. The regression of
human keypoints and association vectors are dependent on
each other and share information from the previous block.
With the operator of element-wise addition, the Keypoints
Path (KP) leverages features before and after the feature fusion/transition within a DPN block. The association vectors in the Association Path (AP), however, accumulate features over blocks to further exploit spatial interdependencies. They are regressed with accumulated channels of feature maps from all previous DPN blocks. With such representation, features from the AP path is less constrained
and more flexible than the KP path. It enforces the AP path

to learn features at a higher level compared to the KP path,
even though they are dependent and share common features.
It is declared in [8] that most of their false positives come
from imprecise localization, other than background confusion and that there is more improvement space in capturing
spatial dependencies than in recognizing body parts appearances. Therefore, we set the learning rate for the VGG layers to be zero, thus maintaining the same low-level visual
features as that used in the openpose model. In this way, we
can purely compare the capability of the networks in capturing spatial dependencies.
The network from the first stage produces a set of keypoint heatmaps S1 = ρ1 (F) and a set of PAFs L1 = φ1 (F),
where ρ1 and φ1 represent high-dimensional functions of
the KP path and AP path networks. To guide the network
to iteratively predict keypoint heatmaps and PAFs at each
stage, we apply two loss functions in each sub-network. We
use an L2 loss between the estimated predictions and the
groundtruth maps and fields. Specifically, the loss functions
for the dual paths at stage t are:
fSt

=

J X
X

kStj (p) − S∗j (p)k22 ,

(1)

kLtc (p) − L∗c (p)k22 ,

(2)

j=1 p

fLt

=

C X
X

4.1. Algorithm Performance Analysis
Average Precision We compare the AP scores of the
proposed network with openpose on the validation set. All
experiments are performed on the local machine with the
same resolution and single scale.
Method
Head Sho. Elb. Wri. Hip Knee Ank. Total
openpose@6stages 46.8 76.4 68.7 54.7 63.6 59.6 52.8 59.5
openpose@3stages 45.5 72.1 63.1 48.1 58.4 51.9 45.8 54.4
Ours@3stages
47.5 76.3 67.6 53.3 62.9 57.9 49.7 58.5
Table 2. Comparisons of Average Precision (AP) scores on the
PoseTrack validation set. Experiments are performed at the same
original resolution and single scale.

Speed and Model Size Comparison We test and compare the networks by averaged forward time for a single
frame. The unit is miliseconds (ms). Both experiments are
performed at the same original resolution and single scale.
Method
forward time (ms)
openpose@6stages
155.8
Ours@3stages
186.6
Table 3. Comparisons of feedforward time in miliseconds (ms) of
different networks for a single frame. Evaluations are performed
with a single Pascal TITAN X GPU.

c=1 p

where S∗j is the groundtruth keypoint heatmap, L∗c is the
groundtruth PAF vector field, at an image location p.

4. Experimental Results
Dataset The PoseTrack [3] dataset consists of over
68, 000 frames. The workshop is organized around a challenge with three competition tracks focusing on single
frame multi-person pose estimation, multi-person pose estimation in videos, and multi-person articulated tracking. In
our work, we focus on the single frame multi-person pose
estimation.
Experimental Settings In order to make a fair comparison with the openpose network, which is trained on the MS
COCO dataset [1], we use the same training data before
testing on the PoseTrack dataset. In our experiments, all the
experiment settings including the testing scales and parameters in the data association techniques are uniform for the
two networks. Therefore, no special tuning on the training
and testing for the PoseTrack dataset is made.
Quantitative Results We report our Average Precision
(AP) scores on the PoseTrack test set1 . The result on test
set is performed at 3 stages and 2 scales (1, 0.75).
Method Head Sho. Elb. Wri. Hip Knee Ank. Total
Ours
48.2 75.4 68.8 59.5 63.6 60.1 53.9 62.4
Table 1. Average Precision (AP) scores on the PoseTrack test set.

Method 3 stages 4 stages 5 stages 6 stages
openpose 103.8 139.0 174.1 209.3
Ours
43.7
50.1
56.4
62.7
Table 4. Comparisons of model size of different networks in Mega
Bytes (MB). Both models have the same input image resolution
and share the same VGG-16 layers.

Even though our model is much smaller than the openpose model, the intermediate storage of network strucures
including accumulated feature maps from AP path consume
GPU memory greatly in current Caffe [17] version. In the
future, by porting memory-efficient denseNet implementation from other deep learning frameworks [5, 4] into Caffe,
which enables more stages of DPN to fit into the GPU memory, we believe the performance will potentially be better.

5. Conclusion
In this work, we propose a dual-path network specially
designed for multi-person human pose estimation, and compare our performance with the openpose[8] network in aspects of model size, forward speed, and estimation accuracy. Extentive experiments on PoseTrack challenge dataset
show that our method is both accurate and efficient. Even
though the method described in this work regresses PAFs[8]
as the association vector, the dual-path network is generic
and not limited to specific vector representation and association techniques.

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Notes
1 Performance

on test set is evalutaed by the PoseTrack server. Challenge results available at: https://posetrack.net/workshops/
iccv2017/posetrack-challenge-results.html

