Publications by Saeed Saadatnejad

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Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication lin ...

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 ...

Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by providing public benchmarks. While state-of-the-art methods are impressive, i.e. ...

In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This is the reason why forecasting pedestrians' intentions sufficiently in advance is one of the most critical and challenging tasks for autonomous vehicles. This w ...

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

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 ...

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 ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

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 ...

Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory predicti ...

Pedestrian image generation in the desired pose can be used in a wide range of applications e.g., person re-identification and tracking which are among the fundamental challenges in self-driving cars. This is a hard task because it should be invariant to a ...

Image synthesis in the desired semantic can be used in many tasks of self-driving cars giving us the possibility to enhance existing challenging datasets by realistic-looking images which we do not have enough. Our goal is to improve the image quality gene ...

Predicting future trajectories is critical in autonomous driving, especially in preventing accidents involving humans, where a predictive agent’s ability to anticipate in advance is of utmost importance. Trajectory forecasting models, employed in fields su ...

Human pose forecasting is inherently multimodal since multiple futures exist for an observed pose sequence. However, evaluating multimodality is challenging since the task is illposed. Therefore, we first propose an alternative paradigm to make the task we ...

Saeed Saadatnejad

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Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Modified Joint Channel-and-Data Estimation for One-Bit Massive MIMO

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

Vehicle trajectory prediction works, but not everywhere

Pedestrian Intention Prediction: A Multi-task Perspective

Pedestrian 3D Bounding Box Prediction

A Shared Representation for Photorealistic Driving Simulators

Toward Reliable Human Pose Forecasting With Uncertainty

Social-Transmotion: Promptable Human Trajectory Prediction

Learning Decoupled Representations for Human Pose Forecasting

Certified Human Trajectory Prediction

Pedestrian Image Generation for Self-driving Cars

Next Steps for Image Synthesis using Semantic Segmentation

JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds

HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario

MotionMap: Representing Multimodality in Human Pose Forecasting