22 research outputs found
Loss Guided Activation for Action Recognition in Still Images
One significant problem of deep-learning based human action recognition is
that it can be easily misled by the presence of irrelevant objects or
backgrounds. Existing methods commonly address this problem by employing
bounding boxes on the target humans as part of the input, in both training and
testing stages. This requirement of bounding boxes as part of the input is
needed to enable the methods to ignore irrelevant contexts and extract only
human features. However, we consider this solution is inefficient, since the
bounding boxes might not be available. Hence, instead of using a person
bounding box as an input, we introduce a human-mask loss to automatically guide
the activations of the feature maps to the target human who is performing the
action, and hence suppress the activations of misleading contexts. We propose a
multi-task deep learning method that jointly predicts the human action class
and human location heatmap. Extensive experiments demonstrate our approach is
more robust compared to the baseline methods under the presence of irrelevant
misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP)
on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\%
relative improvements over the best results reported in the literature, and
thus set new state-of-the-art results. Additionally, unlike some existing
methods, we eliminate the requirement of using a person bounding box as an
input during testing.Comment: Accepted to appear in ACCV 201
STAR: Sparse Trained Articulated Human Body Regressor
The SMPL body model is widely used for the estimation, synthesis, and
analysis of 3D human pose and shape. While popular, we show that SMPL has
several limitations and introduce STAR, which is quantitatively and
qualitatively superior to SMPL. First, SMPL has a huge number of parameters
resulting from its use of global blend shapes. These dense pose-corrective
offsets relate every vertex on the mesh to all the joints in the kinematic
tree, capturing spurious long-range correlations. To address this, we define
per-joint pose correctives and learn the subset of mesh vertices that are
influenced by each joint movement. This sparse formulation results in more
realistic deformations and significantly reduces the number of model parameters
to 20% of SMPL. When trained on the same data as SMPL, STAR generalizes better
despite having many fewer parameters. Second, SMPL factors pose-dependent
deformations from body shape while, in reality, people with different shapes
deform differently. Consequently, we learn shape-dependent pose-corrective
blend shapes that depend on both body pose and BMI. Third, we show that the
shape space of SMPL is not rich enough to capture the variation in the human
population. We address this by training STAR with an additional 10,000 scans of
male and female subjects, and show that this results in better model
generalization. STAR is compact, generalizes better to new bodies and is a
drop-in replacement for SMPL. STAR is publicly available for research purposes
at http://star.is.tue.mpg.de.Comment: ECCV 202