Publications by Taylor Ferdinand Mordan

Toward Reliable Human Pose Forecasting With Uncertainty

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan, Parham Saremi

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

2024

A generic diffusion-based approach for 3D human pose prediction in the wild

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan

Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach th ...

IEEE2023

Simple Yet Effective Action Recognition for Autonomous Driving

Alexandre Massoud Alahi, Lorenzo Bertoni, Taylor Ferdinand Mordan

Self-driving cars and delivery robots are set to shape the future of transportation, but they still have to learn how to co-exist with humans in close proximity. Autonomous systems need to detect pedestrians and understand the meaning of their actions befo ...

2022

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. The ...

IEEE Institute of Electrical and Electronics Engineers2022

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Alexandre Massoud Alahi, Taylor Ferdinand Mordan, Dongxu Guo

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

2022

Learning Decoupled Representations for Human Pose Forecasting

Alexandre Massoud Alahi, Yuejiang Liu, Behnam Parsaeifard, Saeed Saadatnejad, Taylor Ferdinand Mordan

Human pose forecasting involves complex spatiotemporal interactions between body parts (e.g., arms, legs, spine). State-of-the-art approaches use Long Short-Term Memories (LSTMs) or Variational AutoEncoders (VAEs) to solve the problem. Yet, they do not eff ...

IEEE2021

MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni, Taylor Ferdinand Mordan

Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots. However, they are usually developed independently and have their respective strengths and limitations. We propose a no ...

IEEE2021

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari, Taylor Ferdinand Mordan, Bastien Germain C. Van Delft

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

2021

Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...

2021

A Shared Representation for Photorealistic Driving Simulators

Alexandre Massoud Alahi, Saeed Saadatnejad, Siyuan Li, Taylor Ferdinand Mordan

A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidel ...

IEEE Institute of Electrical and Electronics Engineers2021

Bilinear Multimodal Discriminator for Adversarial Domain Adaptation with Privileged Information

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Over the past few years, deep Convolutional Neural Networks have shown outstanding performance on semantic segmentation, which is an essential tool needed by self-driving cars to understand their environments. However, their training relies on large datase ...

2020

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. These encompass visual ...

IEEE2020