850 citations · 1k across the 14 of their papers we have counts for
15 papers
Recognizing Co-Speech Gestures in-the-Wild
Sindhu B Hegde, K R Prajwal, Andrew Zisserman
While humans naturally gesture during speech, only a sparse subset of these co-speech gestures are visually depictive and semantically linked to specific spoken words. In this pape…
Understanding Co-speech Gestures in-the-wild
Sindhu B Hegde, K R Prajwal, Taein Kwon +1
Co-speech gestures play a vital role in non-verbal communication. In this paper, we introduce a new framework for co-speech gesture understanding in the wild. Specifically, we prop…
New keypoint-based approach for recognising British Sign Language (BSL) from sequences
Oishi Deb, KR Prajwal, Andrew Zisserman
In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's perfo…
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
K R Prajwal, Bowen Shi, Matthew Lee +8
We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music aud…
A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision
Charles Raude, K R Prajwal, Liliane Momeni +4
In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer…
Weakly-supervised Fingerspelling Recognition in British Sign Language Videos
K R Prajwal, Hannah Bull, Liliane Momeni +3
The goal of this work is to detect and recognize sequences of letters signed using fingerspelling in British Sign Language (BSL). Previous fingerspelling recognition methods have n…
Lip-to-Speech Synthesis for Arbitrary Speakers in the Wild
Sindhu B Hegde, K R Prajwal, Rudrabha Mukhopadhyay +2
In this work, we address the problem of generating speech from silent lip videos for any speaker in the wild. In stark contrast to previous works, our method (i) is not restricted…
Automatic dense annotation of large-vocabulary sign language videos
Liliane Momeni, Hannah Bull, K R Prajwal +3
Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the aud…
Visual Keyword Spotting with Attention
K R Prajwal, Liliane Momeni, Triantafyllos Afouras +1
In this paper, we consider the task of spotting spoken keywords in silent video sequences -- also known as visual keyword spotting. To this end, we investigate Transformer-based mo…
Sub-word Level Lip Reading With Visual Attention
K R Prajwal, Triantafyllos Afouras, Andrew Zisserman
The goal of this paper is to learn strong lip reading models that can recognise speech in silent videos. Most prior works deal with the open-set visual speech recognition problem b…
Visual Speech Enhancement Without A Real Visual Stream
Sindhu B Hegde, K R Prajwal, Rudrabha Mukhopadhyay +2
In this work, we re-think the task of speech enhancement in unconstrained real-world environments. Current state-of-the-art methods use only the audio stream and are limited in the…
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
K R Prajwal, Rudrabha Mukhopadhyay, Vinay Namboodiri +1
In this work, we investigate the problem of lip-syncing a talking face video of an arbitrary identity to match a target speech segment. Current works excel at producing accurate li…
Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis
K R Prajwal, Rudrabha Mukhopadhyay, Vinay Namboodiri +1
Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip t…
Towards Automatic Face-to-Face Translation
Prajwal K R, Rudrabha Mukhopadhyay, Jerin Philip +3
In light of the recent breakthroughs in automatic machine translation systems, we propose a novel approach that we term as "Face-to-Face Translation". As today's digital communicat…
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Sripad Krishna Devalla, Prajwal K. Renukanand, Bharathwaj K. Sreedhar +8
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneou…