Search Results for author: Aditya Agarwal

Found 5 papers, 0 papers with code

INR-V: A Continuous Representation Space for Video-based Generative Tasks

no code implementations29 Oct 2022 Bipasha Sen, Aditya Agarwal, Vinay P Namboodiri, C. V. Jawahar

In this work, we evaluate the space learned by INR-V on diverse generative tasks such as video interpolation, novel video generation, video inversion, and video inpainting against the existing baselines.

Video Generation Video Inpainting

FaceOff: A Video-to-Video Face Swapping System

no code implementations21 Aug 2022 Aditya Agarwal, Bipasha Sen, Rudrabha Mukhopadhyay, Vinay Namboodiri, C. V. Jawahar

To tackle this challenge, we introduce video-to-video (V2V) face-swapping, a novel task of face-swapping that can preserve (1) the identity and expressions of the source (actor) face video and (2) the background and pose of the target (double) video.

Face Swapping

Towards MOOCs for Lipreading: Using Synthetic Talking Heads to Train Humans in Lipreading at Scale

no code implementations21 Aug 2022 Aditya Agarwal, Bipasha Sen, Rudrabha Mukhopadhyay, Vinay Namboodiri, C. V Jawahar

Because of the manual pipeline, such platforms are also limited in vocabulary, supported languages, accents, and speakers and have a high usage cost.

Lipreading Lip Reading

Personalized One-Shot Lipreading for an ALS Patient

no code implementations2 Nov 2021 Bipasha Sen, Aditya Agarwal, Rudrabha Mukhopadhyay, Vinay Namboodiri, C V Jawahar

Apart from evaluating our approach on the ALS patient, we also extend it to people with hearing impairment relying extensively on lip movements to communicate.

Domain Adaptation Lipreading

PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation

no code implementations1 Aug 2020 Aditya Agarwal, Yupeng Han, Maxim Likhachev

We show that our approach can achieve a speedup of 100x over PERCH, as well as better accuracy than the state-of-the-art data-driven approaches on 6-DoF pose estimation without the need for annotating ground truth poses in the training data.

Pose Estimation Robotic Grasping

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