Paper Info
Title
Photorealistic Adaptation And Interpolation Of Facial Expressions Using Hmms And Aams For Audio-Visual Speech Synthesis
Abstract
In this paper, motivated by the continuously increasing presence of intelligent agents in everyday life, we address the problem of expressive photorealistic audio-visual speech synthesis, with a strong focus on the visual modality. Emotion constitutes one of the main driving factors of social life and it is expressed mainly through facial expressions. Synthesis of a talking head capable of expressive audio-visual speech is challenging due to the data overhead that arises when considering the vast number of emotions we would like the talking head to express. In order to tackle this challenge, we propose the usage of two methods, namely Hidden Markov Model (HMM) adaptation and interpolation, with HMMs modeling visual parameters via an Active Appearance Model (AAM) of the face. We show that through HMM adaptation we can successfully adapt a "neutral" talking head to a target emotion with a small amount of adaptation data, as well as that through HMM interpolation we can robustly achieve different levels of intensity for an emotion.
Year
Venue
Keywords
2017
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
expressive audio-visual speech synthesis, photorealistic, hidden Markov model, interpolation, adaptation
Field
DocType
ISSN
Visual modality,Intelligent agent,Computer science,Interpolation,Artificial intelligence,Computer vision,Speech synthesis,Pattern recognition,Visualization,Active appearance model,Speech recognition,Facial expression,Hidden Markov model
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Panagiotis Paraskevas Filntisis143.92
Athanasios Katsamanis230122.71
Petros Maragos33733591.97