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DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models (IJCAI 2023) | The DiffuseStyleGesture+ entry to the GENEA Challenge 2023 (ICMI 2023, Reproducibility Award)
We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEATX (BEAT-SMPLXFLAME), a new mesh-level holistic co-speech dataset.
The goal of project is focus on Audio-driven Gesture Generation with output is 3D keypoints gesture. Input: Audio, Text, Gesture, ..etc. -> Output: Gesture Motion. Gesture Generation is the process of generating gestures from speech or text.
This paper presents a novel framework for speech-driven gesture production, applicable to virtual agents to enhance human-computer interaction. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning.
The goal of project is focus on Audio-driven Gesture Generation with output is 3D keypoints gesture. Input: Audio, Text, Gesture, ..etc. -> Output: Gesture Motion. Gesture Generation is the process of generating gestures from speech or text.
8 maj 2023 · To address these problems, we present DiffuseStyleGesture, a diffusion model based speech-driven gesture generation approach. It generates high-quality, speech-matched, stylized, and diverse co-speech gestures based on given speeches of arbitrary length.
While most existing methods can generate gestures from audio directly, they usually overlook that emotion is one of the key factors of authentic co-speech gesture generation. In this work, we propose EmotionGesture, a novel framework for synthesizing vivid and diverse emotional co-speech 3D gestures from audio.
