750 research outputs found
A Generative Model of People in Clothing
We present the first image-based generative model of people in clothing for
the full body. We sidestep the commonly used complex graphics rendering
pipeline and the need for high-quality 3D scans of dressed people. Instead, we
learn generative models from a large image database. The main challenge is to
cope with the high variance in human pose, shape and appearance. For this
reason, pure image-based approaches have not been considered so far. We show
that this challenge can be overcome by splitting the generating process in two
parts. First, we learn to generate a semantic segmentation of the body and
clothing. Second, we learn a conditional model on the resulting segments that
creates realistic images. The full model is differentiable and can be
conditioned on pose, shape or color. The result are samples of people in
different clothing items and styles. The proposed model can generate entirely
new people with realistic clothing. In several experiments we present
encouraging results that suggest an entirely data-driven approach to people
generation is possible
Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
Traffic accident anticipation aims to predict accidents from dashcam videos
as early as possible, which is critical to safety-guaranteed self-driving
systems. With cluttered traffic scenes and limited visual cues, it is of great
challenge to predict how long there will be an accident from early observed
frames. Most existing approaches are developed to learn features of
accident-relevant agents for accident anticipation, while ignoring the features
of their spatial and temporal relations. Besides, current deterministic deep
neural networks could be overconfident in false predictions, leading to high
risk of traffic accidents caused by self-driving systems. In this paper, we
propose an uncertainty-based accident anticipation model with spatio-temporal
relational learning. It sequentially predicts the probability of traffic
accident occurrence with dashcam videos. Specifically, we propose to take
advantage of graph convolution and recurrent networks for relational feature
learning, and leverage Bayesian neural networks to address the intrinsic
variability of latent relational representations. The derived uncertainty-based
ranking loss is found to significantly boost model performance by improving the
quality of relational features. In addition, we collect a new Car Crash Dataset
(CCD) for traffic accident anticipation which contains environmental attributes
and accident reasons annotations. Experimental results on both public and the
newly-compiled datasets show state-of-the-art performance of our model. Our
code and CCD dataset are available at https://github.com/Cogito2012/UString.Comment: Accepted by ACM MM 202
Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps
Motion prediction is a challenging task for autonomous vehicles due to
uncertainty in the sensor data, the non-deterministic nature of future, and
complex behavior of agents. In this paper, we tackle this problem by
representing the scene as dynamic occupancy grid maps (DOGMs), associating
semantic labels to the occupied cells and incorporating map information. We
propose a novel framework that combines deep-learning-based spatio-temporal and
probabilistic approaches to predict vehicle behaviors.Contrary to the
conventional OGM prediction methods, evaluation of our work is conducted
against the ground truth annotations. We experiment and validate our results on
real-world NuScenes dataset and show that our model shows superior ability to
predict both static and dynamic vehicles compared to OGM predictions.
Furthermore, we perform an ablation study and assess the role of semantic
labels and map in the architecture.Comment: Accepted to the 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2023
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