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Finding the best way to put media bias research into practice via an annotation app ...
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| 2 |
Multimodal Lip-Reading for Tracheostomy Patients in the Greek Language
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In: Computers; Volume 11; Issue 3; Pages: 34 (2022)
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Towards Portuguese Sign Language Identification Using Deep Learning
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| 4 |
SonAmi: A Tangible Creativity Support Tool for Productive Procrastination
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In: C&C ’21 - 13th ACM Conference on Creativity & Cognition ; https://hal.inria.fr/hal-03442565 ; C&C ’21 - 13th ACM Conference on Creativity & Cognition, Jun 2021, Virtual Event, Italy. pp.1-10, ⟨10.1145/3450741.3465250⟩ (2021)
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A global-scale screening of non-native aquatic organisms to identify potentially invasive species under current and future climate conditions
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In: ISSN: 0048-9697 ; EISSN: 1879-1026 ; Science of the Total Environment ; https://hal.univ-lorraine.fr/hal-03544887 ; Science of the Total Environment, Elsevier, 2021, 788, pp.147868. ⟨10.1016/j.scitotenv.2021.147868⟩ (2021)
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Toucher le son d’avant d’écrire (Touching Sound, Learning Writing)
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In: Actes ERGO'IA 2021 ; ERGO'IA 2021 ; https://hal.archives-ouvertes.fr/hal-03365473 ; ERGO'IA 2021, Oct 2021, Bidart, France (2021)
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Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences
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In: ISSN: 1991-959X ; Geoscientific Model Development ; https://hal.archives-ouvertes.fr/hal-03188035 ; Geoscientific Model Development, European Geosciences Union, 2021, 14, pp.1639 - 1656. ⟨10.5194/gmd-14-1639-2021⟩ (2021)
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| 8 |
Dictionaries Integrated into English Learning Apps: Critical Comments and Suggestions for Improvement
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In: Lexikos; Vol. 31 (2021); 68-92 ; 2224-0039 (2021)
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| 9 |
Clitics are not enough: on agreement and null subjects in Brazilian Venetan
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In: Glossa: a journal of general linguistics; Vol 6, No 1 (2021); 86 ; 2397-1835 (2021)
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| 10 |
Minimax Feature Merge: The Featural Linguistic Turing Machine ...
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| 11 |
Minimax Feature Merge: The Featural Linguistic Turing Machine ...
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| 12 |
Enter the Matrix: What the new brain-computer interfaces teach us about agency, privacy, and human subjectivity
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In: The iJournal: Graduate Student Journal of the Faculty of Information; Vol 6 No 2 (2021): Spring 2021 ; 2561-7397 (2021)
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| 13 |
Voice-user interfaces for TESOL: Potential and receptiveness among native and non-native English speaking instructors
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Kent, David. - : University of Hawaii National Foreign Language Resource Center, 2021. : Center for Language & Technology, 2021. : (co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin), 2021
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| 14 |
Evolution of human computer interaction
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In: Sci. Visualization ; Scientific Visualization (2021)
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| 15 |
Natural Language Processing for Lexical Corpus Analysis
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In: Doctoral Dissertations (2021)
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Audio-driven Character Animation
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In: Doctoral Dissertations (2021)
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Abstract:
Generating believable character animations is a fundamentally important problem in the field of computer graphics and computer vision. It also has a diverse set of applications ranging from entertainment (e.g., films, games), medicine (e.g., facial therapy and prosthetics), mixed reality, and education (e.g., language/speech training and cyber-assistants). All these applications are all empowered by the ability to model and animate characters convincingly (human or non-human). Existing key-framing or performance capture approaches used for creating animations, especially facial animations, are either laborious or hard to edit. In particular, producing expressive animations from input speech automatically remains an open challenge. In this thesis, I propose novel deep-learning based approaches to produce speech audio-driven character animations, including talking-head animations for character face rigs and portrait images, and reenacted gesture animations for natural human speech videos. First, I propose a neural network architecture, called VisemeNet, that can automatically animate an input face rig using audio as input. The network has three stages: one that learns to predict a sequence of phoneme-groups from audio; another that learns to predict the geometric location of important facial landmarks from audio; and a final stage that combines the outcome from previous stages to produce animation motion curves for FACS-based (Facial Action Coding System-based) face rigs. Second, I propose MakeItTalk, a method that takes as input a portrait image of a face along with audio, and produces the expressive synchronized talking-head animation. The portrait image can range from artistic cartoons to real human faces. In addition, the method generates the whole head motion dynamics matching the audio stresses and pauses. The key insight of the method is to disentangle the content and speaker identity in the input audio signals, and drive the animation from both of them. The content is used for robust synchronization of lips and nearby facial regions. The speaker information is used to capture the rest of the facial expressions and head motion dynamics that are important for generating expressive talking head animations. I also show that MakeItTalk can generalize to new audio clips and face images not seen during training. Both VisemeNet and MakeItTalk lead to much more expressive talking-head animations with higher overall quality compared to the state-of-the-art. Lastly, I propose a method that generates speech gesture animation by reenacting a given video to match a target speech audio. The key idea is to split and re-assemble clips from an existing reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, I propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, the method incorporates an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. The method generates reenactments that are consistent with both the audio rhythms and the speech content. The resulting synthesized videos have much higher quality and consistency with the target audio compared to previous work and baselines.
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Keyword:
Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
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URL: https://scholarworks.umass.edu/dissertations_2/2393
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=3336&context=dissertations_2
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| 17 |
Zeitgeist: Modelando um projeto editorial com interface digital
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In: Pandaemonium Germanicum: Revista de Estudos Germanísticos, Vol 24, Iss 42 (2021) (2021)
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| 18 |
Dictionaries Integrated into English Learning Apps: Critical Comments and Suggestions for Improvement
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In: Lexikos, Vol 31, Pp 68-92 (2021) (2021)
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| 19 |
Explaining Rainfall Accumulations over Several Days in the French Alps Using Low-Dimensional Atmospheric Predictors Based on Analogy
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In: ISSN: 1558-8424 ; EISSN: 1558-8432 ; Journal of Applied Meteorology and Climatology ; https://hal.archives-ouvertes.fr/hal-03087661 ; Journal of Applied Meteorology and Climatology, American Meteorological Society, 2020, 59 (2), pp.237-250. ⟨10.1175/JAMC-D-19-0112.1⟩ (2020)
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| 20 |
Voks: Digital instruments for chironomic control of voice samples
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-03009712 ; Speech Communication, Elsevier : North-Holland, 2020, 125, pp.97 - 113. ⟨10.1016/j.specom.2020.10.002⟩ (2020)
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