Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Mittal, Trisha"'
Recent advancements in Artificial Intelligence have led to remarkable improvements in generating realistic human faces. While these advancements demonstrate significant progress in generative models, they also raise concerns about the potential misus
Externí odkaz:
http://arxiv.org/abs/2409.15498
Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for providing a rich interactive experience. Most prior works assess the quality of generated head motion by comparing them against a single ground
Externí odkaz:
http://arxiv.org/abs/2210.14800
As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is increasing. This phenomenon causes the spread of misinformation, creating a
Externí odkaz:
http://arxiv.org/abs/2207.13064
Autor:
Mittal, Trisha, Mathur, Puneet, Chandra, Rohan, Bhatt, Apurva, Gupta, Vikram, Mukherjee, Debdoot, Bera, Aniket, Manocha, Dinesh
We present a computational approach for estimating emotion contagion on social media networks. Built on a foundation of psychology literature, our approach estimates the degree to which the perceivers' emotional states (positive or negative) start to
Externí odkaz:
http://arxiv.org/abs/2207.07165
Autor:
Gupta, Vikram, Mittal, Trisha, Mathur, Puneet, Mishra, Vaibhav, Maheshwari, Mayank, Bera, Aniket, Mukherjee, Debdoot, Manocha, Dinesh
We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K u
Externí odkaz:
http://arxiv.org/abs/2203.14456
We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas
Externí odkaz:
http://arxiv.org/abs/2103.06541
We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram~(EEG) and Eye movement~(EM) data in order to help differentiate between normal reading and task-oriented reading. Understanding the physiol
Externí odkaz:
http://arxiv.org/abs/2102.11922
Autor:
Bhattacharya, Uttaran, Rewkowski, Nicholas, Guhan, Pooja, Williams, Niall L., Mittal, Trisha, Bera, Aniket, Manocha, Dinesh
Publikováno v:
ISMAR, 2020, pp. 24-35
We present a novel autoregression network to generate virtual agents that convey various emotions through their walking styles or gaits. Given the 3D pose sequences of a gait, our network extracts pertinent movement features and affective features fr
Externí odkaz:
http://arxiv.org/abs/2010.01615
We present MCQA, a learning-based algorithm for multimodal question answering. MCQA explicitly fuses and aligns the multimodal input (i.e. text, audio, and video), which forms the context for the query (question and answer). Our approach fuses and al
Externí odkaz:
http://arxiv.org/abs/2004.12238
We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additional
Externí odkaz:
http://arxiv.org/abs/2003.06711