Passing a Non-verbal Turing Test: Evaluating Gesture Animations Generated from Speech

• URL: http://arxiv.org/abs/2107.00712v1
• Date: Thu, 1 Jul 2021 19:38:43 GMT
• Title: Passing a Non-verbal Turing Test: Evaluating Gesture Animations Generated from Speech
• Authors: Manuel Rebol and Christian G\"utl and Krzysztof Pietroszek
• Abstract summary: In this paper, we propose a novel, data-driven technique for generating gestures directly from speech. Our approach is based on the application of Generative Adversarial Neural Networks (GANs) to model the correlation rather than causation between speech and gestures. For the study, we animate the generated gestures on a virtual character. We find that users are not able to distinguish between the generated and the recorded gestures.
• Score: 6.445605125467574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real life, people communicate using both speech and non-verbal signals such as gestures, face expression or body pose. Non-verbal signals impact the meaning of the spoken utterance in an abundance of ways. An absence of non-verbal signals impoverishes the process of communication. Yet, when users are represented as avatars, it is difficult to translate non-verbal signals along with the speech into the virtual world without specialized motion-capture hardware. In this paper, we propose a novel, data-driven technique for generating gestures directly from speech. Our approach is based on the application of Generative Adversarial Neural Networks (GANs) to model the correlation rather than causation between speech and gestures. This approach approximates neuroscience findings on how non-verbal communication and speech are correlated. We create a large dataset which consists of speech and corresponding gestures in a 3D human pose format from which our model learns the speaker-specific correlation. We evaluate the proposed technique in a user study that is inspired by the Turing test. For the study, we animate the generated gestures on a virtual character. We find that users are not able to distinguish between the generated and the recorded gestures. Moreover, users are able to identify our synthesized gestures as related or not related to a given utterance.

Related papers

• Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech [29.510756530126837]
  We introduce a data-driven method to visually represent articulator motion in MRI videos of the human vocal tract during speech. We leverage large pre-trained speech models, which are embedded with prior knowledge, to generalize the visual domain to unseen data.
  arXiv  Detail & Related papers  (2024-09-23T20:19:24Z)
• Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speech Affective Face and Body Expressions from Affordable Inputs [67.27840327499625]
  We present a multimodal learning-based method to simultaneously synthesize co-speech facial expressions and upper-body gestures for digital characters. Our approach learns from sparse face landmarks and upper-body joints, estimated directly from video data, to generate plausible emotive character motions.
  arXiv  Detail & Related papers  (2024-06-26T04:53:11Z)
• ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis [50.69464138626748]
  We present ConvoFusion, a diffusion-based approach for multi-modal gesture synthesis. Our method proposes two guidance objectives that allow the users to modulate the impact of different conditioning modalities. Our method is versatile in that it can be trained either for generating monologue gestures or even the conversational gestures.
  arXiv  Detail & Related papers  (2024-03-26T17:59:52Z)
• From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations [107.88375243135579]
  Given speech audio, we output multiple possibilities of gestural motion for an individual, including face, body, and hands. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures. Experiments show our model generates appropriate and diverse gestures, outperforming both diffusion- and VQ-only methods.
  arXiv  Detail & Related papers  (2024-01-03T18:55:16Z)
• Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents [5.244401764969407]
  Embodied agents, in the form of virtual agents or social robots, are rapidly becoming more widespread. We propose a novel framework that can generate sequences of joint angles from the speech text and speech audio utterances.
  arXiv  Detail & Related papers  (2023-09-17T18:46:25Z)
• Audio-Driven Co-Speech Gesture Video Generation [92.15661971086746]
  We define and study this challenging problem of audio-driven co-speech gesture video generation. Our key insight is that the co-speech gestures can be decomposed into common motion patterns and subtle rhythmic dynamics. We propose a novel framework, Audio-driveN Gesture vIdeo gEneration (ANGIE), to effectively capture the reusable co-speech gesture patterns.
  arXiv  Detail & Related papers  (2022-12-05T15:28:22Z)
• Learning Speech-driven 3D Conversational Gestures from Video [106.15628979352738]
  We propose the first approach to automatically and jointly synthesize both the synchronous 3D conversational body and hand gestures. Our algorithm uses a CNN architecture that leverages the inherent correlation between facial expression and hand gestures. We also contribute a new way to create a large corpus of more than 33 hours of annotated body, hand, and face data from in-the-wild videos of talking people.
  arXiv  Detail & Related papers  (2021-02-13T01:05:39Z)
• Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity [21.61168067832304]
  We present an automatic gesture generation model that uses the multimodal context of speech text, audio, and speaker identity to reliably generate gestures. Experiments with the introduced metric and subjective human evaluation showed that the proposed gesture generation model is better than existing end-to-end generation models.
  arXiv  Detail & Related papers  (2020-09-04T11:42:45Z)
• "Notic My Speech" -- Blending Speech Patterns With Multimedia [65.91370924641862]
  We propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. Our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. We show that there is a strong correlation between our model's understanding of multi-view speech and the human perception.
  arXiv  Detail & Related papers  (2020-06-12T06:51:55Z)
• Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation of Facial Gestures in Dyadic Settings [11.741529272872219]
  To enable more natural face-to-face interactions, conversational agents need to adapt their behavior to their interlocutors. Most existing gesture-generating systems do not utilize multi-modal cues from the interlocutor when synthesizing non-verbal behavior. We introduce a probabilistic method to synthesize interlocutor-aware facial gestures in dyadic conversations.
  arXiv  Detail & Related papers  (2020-06-11T14:11:51Z)
• Gesticulator: A framework for semantically-aware speech-driven gesture generation [17.284154896176553]
  We present a model designed to produce arbitrary beat and semantic gestures together. Our deep-learning based model takes both acoustic and semantic representations of speech as input, and generates gestures as a sequence of joint angle rotations as output. The resulting gestures can be applied to both virtual agents and humanoid robots.
  arXiv  Detail & Related papers  (2020-01-25T14:42:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.