2,047 research outputs found
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Recent approaches on trajectory forecasting use tracklets to predict the
future positions of pedestrians exploiting Long Short Term Memory (LSTM)
architectures. This paper shows that adding vislets, that is, short sequences
of head pose estimations, allows to increase significantly the trajectory
forecasting performance. We then propose to use vislets in a novel framework
called MX-LSTM, capturing the interplay between tracklets and vislets thanks to
a joint unconstrained optimization of full covariance matrices during the LSTM
backpropagation. At the same time, MX-LSTM predicts the future head poses,
increasing the standard capabilities of the long-term trajectory forecasting
approaches. With standard head pose estimators and an attentional-based social
pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all
the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic
margin when the pedestrians slow down, a case where most of the forecasting
approaches struggle to provide an accurate solution.Comment: 10 pages, 3 figures to appear in CVPR 201
Multimodal Three Dimensional Scene Reconstruction, The Gaussian Fields Framework
The focus of this research is on building 3D representations of real world scenes and objects using different imaging sensors. Primarily range acquisition devices (such as laser scanners and stereo systems) that allow the recovery of 3D geometry, and multi-spectral image sequences including visual and thermal IR images that provide additional scene characteristics. The crucial technical challenge that we addressed is the automatic point-sets registration task. In this context our main contribution is the development of an optimization-based method at the core of which lies a unified criterion that solves simultaneously for the dense point correspondence and transformation recovery problems. The new criterion has a straightforward expression in terms of the datasets and the alignment parameters and was used primarily for 3D rigid registration of point-sets. However it proved also useful for feature-based multimodal image alignment. We derived our method from simple Boolean matching principles by approximation and relaxation. One of the main advantages of the proposed approach, as compared to the widely used class of Iterative Closest Point (ICP) algorithms, is convexity in the neighborhood of the registration parameters and continuous differentiability, allowing for the use of standard gradient-based optimization techniques. Physically the criterion is interpreted in terms of a Gaussian Force Field exerted by one point-set on the other. Such formulation proved useful for controlling and increasing the region of convergence, and hence allowing for more autonomy in correspondence tasks. Furthermore, the criterion can be computed with linear complexity using recently developed Fast Gauss Transform numerical techniques. In addition, we also introduced a new local feature descriptor that was derived from visual saliency principles and which enhanced significantly the performance of the registration algorithm. The resulting technique was subjected to a thorough experimental analysis that highlighted its strength and showed its limitations. Our current applications are in the field of 3D modeling for inspection, surveillance, and biometrics. However, since this matching framework can be applied to any type of data, that can be represented as N-dimensional point-sets, the scope of the method is shown to reach many more pattern analysis applications
End-to-end Recovery of Human Shape and Pose
We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In
contrast to most current methods that compute 2D or 3D joint locations, we
produce a richer and more useful mesh representation that is parameterized by
shape and 3D joint angles. The main objective is to minimize the reprojection
loss of keypoints, which allow our model to be trained using images in-the-wild
that only have ground truth 2D annotations. However, the reprojection loss
alone leaves the model highly under constrained. In this work we address this
problem by introducing an adversary trained to tell whether a human body
parameter is real or not using a large database of 3D human meshes. We show
that HMR can be trained with and without using any paired 2D-to-3D supervision.
We do not rely on intermediate 2D keypoint detections and infer 3D pose and
shape parameters directly from image pixels. Our model runs in real-time given
a bounding box containing the person. We demonstrate our approach on various
images in-the-wild and out-perform previous optimization based methods that
output 3D meshes and show competitive results on tasks such as 3D joint
location estimation and part segmentation.Comment: CVPR 2018, Project page with code: https://akanazawa.github.io/hmr
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Face Transfer with Multilinear Models
Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities x expressions x visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.Engineering and Applied Science
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and
their head orientations. We argue that people trajectory and head pose
forecasting can be modelled as a joint problem. Recent approaches on trajectory
forecasting leverage short-term trajectories (aka tracklets) of pedestrians to
predict their future paths. In addition, sociological cues, such as expected
destination or pedestrian interaction, are often combined with tracklets. In
this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between
positions and head orientations (vislets) thanks to a joint unconstrained
optimization of full covariance matrices during the LSTM backpropagation. We
additionally exploit the head orientations as a proxy for the visual attention,
when modeling social interactions. MX-LSTM predicts future pedestrians location
and head pose, increasing the standard capabilities of the current approaches
on long-term trajectory forecasting. Compared to the state-of-the-art, our
approach shows better performances on an extensive set of public benchmarks.
MX-LSTM is particularly effective when people move slowly, i.e. the most
challenging scenario for all other models. The proposed approach also allows
for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065
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