MM-Conv: A Multi-modal Conversational Dataset for Virtual Humans
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912052949286912 |
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| author | Deichler, Anna O'Regan, Jim Beskow, Jonas |
| author_facet | Deichler, Anna O'Regan, Jim Beskow, Jonas |
| contents | In this paper, we present a novel dataset captured using a VR headset to record conversations between participants within a physics simulator (AI2-THOR). Our primary objective is to extend the field of co-speech gesture generation by incorporating rich contextual information within referential settings. Participants engaged in various conversational scenarios, all based on referential communication tasks. The dataset provides a rich set of multimodal recordings such as motion capture, speech, gaze, and scene graphs. This comprehensive dataset aims to enhance the understanding and development of gesture generation models in 3D scenes by providing diverse and contextually rich data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00253 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MM-Conv: A Multi-modal Conversational Dataset for Virtual Humans Deichler, Anna O'Regan, Jim Beskow, Jonas Computer Vision and Pattern Recognition Computation and Language Graphics Human-Computer Interaction In this paper, we present a novel dataset captured using a VR headset to record conversations between participants within a physics simulator (AI2-THOR). Our primary objective is to extend the field of co-speech gesture generation by incorporating rich contextual information within referential settings. Participants engaged in various conversational scenarios, all based on referential communication tasks. The dataset provides a rich set of multimodal recordings such as motion capture, speech, gaze, and scene graphs. This comprehensive dataset aims to enhance the understanding and development of gesture generation models in 3D scenes by providing diverse and contextually rich data. |
| title | MM-Conv: A Multi-modal Conversational Dataset for Virtual Humans |
| topic | Computer Vision and Pattern Recognition Computation and Language Graphics Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.00253 |