3,982 research outputs found
Context-aware Human Motion Prediction
The problem of predicting human motion given a sequence of past observations
is at the core of many applications in robotics and computer vision. Current
state-of-the-art formulate this problem as a sequence-to-sequence task, in
which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that
predicts future movements, typically in the order of 1 to 2 seconds. However,
one aspect that has been obviated so far, is the fact that human motion is
inherently driven by interactions with objects and/or other humans in the
environment. In this paper, we explore this scenario using a novel
context-aware motion prediction architecture. We use a semantic-graph model
where the nodes parameterize the human and objects in the scene and the edges
their mutual interactions. These interactions are iteratively learned through a
graph attention layer, fed with the past observations, which now include both
object and human body motions. Once this semantic graph is learned, we inject
it to a standard RNN to predict future movements of the human/s and object/s.
We consider two variants of our architecture, either freezing the contextual
interactions in the future of updating them. A thorough evaluation in the
"Whole-Body Human Motion Database" shows that in both cases, our context-aware
networks clearly outperform baselines in which the context information is not
considered.Comment: Accepted at CVPR2
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
We address the problem of semantic nighttime image segmentation and improve
the state-of-the-art, by adapting daytime models to nighttime without using
nighttime annotations. Moreover, we design a new evaluation framework to
address the substantial uncertainty of semantics in nighttime images. Our
central contributions are: 1) a curriculum framework to gradually adapt
semantic segmentation models from day to night through progressively darker
times of day, exploiting cross-time-of-day correspondences between daytime
images from a reference map and dark images to guide the label inference in the
dark domains; 2) a novel uncertainty-aware annotation and evaluation framework
and metric for semantic segmentation, including image regions beyond human
recognition capability in the evaluation in a principled fashion; 3) the Dark
Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight
images with correspondences to their daytime counterparts plus a set of 201
nighttime images with fine pixel-level annotations created with our protocol,
which serves as a first benchmark for our novel evaluation. Experiments show
that our map-guided curriculum adaptation significantly outperforms
state-of-the-art methods on nighttime sets both for standard metrics and our
uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals
that selective invalidation of predictions can improve results on data with
ambiguous content such as our benchmark and profit safety-oriented applications
involving invalid inputs.Comment: IEEE T-PAMI 202
GAGAN: Geometry-Aware Generative Adversarial Networks
Deep generative models learned through adversarial training have become
increasingly popular for their ability to generate naturalistic image textures.
However, aside from their texture, the visual appearance of objects is
significantly influenced by their shape geometry; information which is not
taken into account by existing generative models. This paper introduces the
Geometry-Aware Generative Adversarial Networks (GAGAN) for incorporating
geometric information into the image generation process. Specifically, in GAGAN
the generator samples latent variables from the probability space of a
statistical shape model. By mapping the output of the generator to a canonical
coordinate frame through a differentiable geometric transformation, we enforce
the geometry of the objects and add an implicit connection from the prior to
the generated object. Experimental results on face generation indicate that the
GAGAN can generate realistic images of faces with arbitrary facial attributes
such as facial expression, pose, and morphology, that are of better quality
than current GAN-based methods. Our method can be used to augment any existing
GAN architecture and improve the quality of the images generated
Unsupervised 3D Pose Estimation with Geometric Self-Supervision
We present an unsupervised learning approach to recover 3D human pose from 2D
skeletal joints extracted from a single image. Our method does not require any
multi-view image data, 3D skeletons, correspondences between 2D-3D points, or
use previously learned 3D priors during training. A lifting network accepts 2D
landmarks as inputs and generates a corresponding 3D skeleton estimate. During
training, the recovered 3D skeleton is reprojected on random camera viewpoints
to generate new "synthetic" 2D poses. By lifting the synthetic 2D poses back to
3D and re-projecting them in the original camera view, we can define
self-consistency loss both in 3D and in 2D. The training can thus be self
supervised by exploiting the geometric self-consistency of the
lift-reproject-lift process. We show that self-consistency alone is not
sufficient to generate realistic skeletons, however adding a 2D pose
discriminator enables the lifter to output valid 3D poses. Additionally, to
learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter
network to allow for an expansion of 2D data. This improves results and
demonstrates the usefulness of 2D pose data for unsupervised 3D lifting.
Results on Human3.6M dataset for 3D human pose estimation demonstrate that our
approach improves upon the previous unsupervised methods by 30% and outperforms
many weakly supervised approaches that explicitly use 3D data
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