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Found 10 papers, 8 papers with code
HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics.
Traffic Accident Risk Forecasting using Contextual Vision Transformers
This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem.
Traffic incident duration prediction via a deep learning framework for text description encoding
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents.
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation
Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events.
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach
This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge.
StylePeople: A Generative Model of Fullbody Human Avatars
We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches.
Point-Based Modeling of Human Clothing
We propose a new approach to human clothing modeling based on point clouds.
Neural Head Reenactment with Latent Pose Descriptors
We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image.
Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation
Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.
Coordinate-based Texture Inpainting for Pose-Guided Image Generation
Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.
