Stochastic Talking Face Generation Using Latent Distribution Matching Article Swipe
Ravindra Yadav
,
Ashish Sardana
,
Vinay P. Namboodiri
,
Rajesh M. Hegde
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.10727
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.10727
The ability to envisage the visual of a talking face based just on hearing a voice is a unique human capability. There have been a number of works that have solved for this ability recently. We differ from these approaches by enabling a variety of talking face generations based on single audio input. Indeed, just having the ability to generate a single talking face would make a system almost robotic in nature. In contrast, our unsupervised stochastic audio-to-video generation model allows for diverse generations from a single audio input. Particularly, we present an unsupervised stochastic audio-to-video generation model that can capture multiple modes of the video distribution. We ensure that all the diverse generations are plausible. We do so through a principled multi-modal variational autoencoder framework. We demonstrate its efficacy on the challenging LRW and GRID datasets and demonstrate performance better than the baseline, while having the ability to generate multiple diverse lip synchronized videos.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2011.10727
- https://arxiv.org/pdf/2011.10727
- OA Status
- green
- References
- 16
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3107511552
All OpenAlex metadata
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https://openalex.org/W3107511552Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2011.10727Digital Object Identifier
- Title
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Stochastic Talking Face Generation Using Latent Distribution MatchingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-11-21Full publication date if available
- Authors
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Ravindra Yadav, Ashish Sardana, Vinay P. Namboodiri, Rajesh M. HegdeList of authors in order
- Landing page
-
https://arxiv.org/abs/2011.10727Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2011.10727Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2011.10727Direct OA link when available
- Concepts
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Autoencoder, Computer science, Face (sociological concept), Variety (cybernetics), Matching (statistics), Artificial intelligence, Speech recognition, Machine learning, Deep learning, Mathematics, Social science, Statistics, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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16Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.GRID | 135 |
| abstract_inverted_index.been | 23 |
| abstract_inverted_index.face | 9, 46, 63 |
| abstract_inverted_index.from | 37, 84 |
| abstract_inverted_index.have | 22, 29 |
| abstract_inverted_index.just | 11, 54 |
| abstract_inverted_index.make | 65 |
| abstract_inverted_index.than | 141 |
| abstract_inverted_index.that | 28, 98, 109 |
| abstract_inverted_index.this | 32 |
| abstract_inverted_index.There | 21 |
| abstract_inverted_index.audio | 51, 87 |
| abstract_inverted_index.based | 10, 48 |
| abstract_inverted_index.human | 19 |
| abstract_inverted_index.model | 79, 97 |
| abstract_inverted_index.modes | 102 |
| abstract_inverted_index.these | 38 |
| abstract_inverted_index.video | 105 |
| abstract_inverted_index.voice | 15 |
| abstract_inverted_index.while | 144 |
| abstract_inverted_index.works | 27 |
| abstract_inverted_index.would | 64 |
| abstract_inverted_index.allows | 80 |
| abstract_inverted_index.almost | 68 |
| abstract_inverted_index.better | 140 |
| abstract_inverted_index.differ | 36 |
| abstract_inverted_index.ensure | 108 |
| abstract_inverted_index.having | 55, 145 |
| abstract_inverted_index.input. | 52, 88 |
| abstract_inverted_index.number | 25 |
| abstract_inverted_index.single | 50, 61, 86 |
| abstract_inverted_index.solved | 30 |
| abstract_inverted_index.system | 67 |
| abstract_inverted_index.unique | 18 |
| abstract_inverted_index.visual | 5 |
| abstract_inverted_index.Indeed, | 53 |
| abstract_inverted_index.ability | 1, 33, 57, 147 |
| abstract_inverted_index.capture | 100 |
| abstract_inverted_index.diverse | 82, 112, 151 |
| abstract_inverted_index.hearing | 13 |
| abstract_inverted_index.nature. | 71 |
| abstract_inverted_index.present | 91 |
| abstract_inverted_index.robotic | 69 |
| abstract_inverted_index.talking | 8, 45, 62 |
| abstract_inverted_index.through | 119 |
| abstract_inverted_index.variety | 43 |
| abstract_inverted_index.videos. | 154 |
| abstract_inverted_index.datasets | 136 |
| abstract_inverted_index.efficacy | 129 |
| abstract_inverted_index.enabling | 41 |
| abstract_inverted_index.envisage | 3 |
| abstract_inverted_index.generate | 59, 149 |
| abstract_inverted_index.multiple | 101, 150 |
| abstract_inverted_index.baseline, | 143 |
| abstract_inverted_index.contrast, | 73 |
| abstract_inverted_index.recently. | 34 |
| abstract_inverted_index.approaches | 39 |
| abstract_inverted_index.framework. | 125 |
| abstract_inverted_index.generation | 78, 96 |
| abstract_inverted_index.plausible. | 115 |
| abstract_inverted_index.principled | 121 |
| abstract_inverted_index.stochastic | 76, 94 |
| abstract_inverted_index.autoencoder | 124 |
| abstract_inverted_index.capability. | 20 |
| abstract_inverted_index.challenging | 132 |
| abstract_inverted_index.demonstrate | 127, 138 |
| abstract_inverted_index.generations | 47, 83, 113 |
| abstract_inverted_index.multi-modal | 122 |
| abstract_inverted_index.performance | 139 |
| abstract_inverted_index.variational | 123 |
| abstract_inverted_index.synchronized | 153 |
| abstract_inverted_index.unsupervised | 75, 93 |
| abstract_inverted_index.Particularly, | 89 |
| abstract_inverted_index.distribution. | 106 |
| abstract_inverted_index.audio-to-video | 77, 95 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |