Publications de Parth Ashit Kothari

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The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, traffic operations, deployment of social robots in crowded environments, to name a fe ...

The ability to forecast human motion, called ``human trajectory forecasting", is a critical requirement for mobility applications such as autonomous driving and robot navigation. Humans plan their path taking into account what might happen in the future. S ...

Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting p ...

Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent ...

Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN- ...

Parth Ashit Kothari

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Collaborative Sampling in Generative Adversarial Networks

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Deep Learning Methods for Socially-Aware Human Trajectory Forecasting

Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Interpretable Social Anchors for Human Trajectory Forecasting in Crowds

Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting

Adversarial Loss for Human Trajectory Prediction