17,480 research outputs found
Learning Speech-driven 3D Conversational Gestures from Video
We propose the first approach to automatically and jointly synthesize both
the synchronous 3D conversational body and hand gestures, as well as 3D face
and head animations, of a virtual character from speech input. Our algorithm
uses a CNN architecture that leverages the inherent correlation between facial
expression and hand gestures. Synthesis of conversational body gestures is a
multi-modal problem since many similar gestures can plausibly accompany the
same input speech. To synthesize plausible body gestures in this setting, we
train a Generative Adversarial Network (GAN) based model that measures the
plausibility of the generated sequences of 3D body motion when paired with the
input audio features. 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. To this end, we apply state-of-the-art monocular
approaches for 3D body and hand pose estimation as well as dense 3D face
performance capture to the video corpus. In this way, we can train on orders of
magnitude more data than previous algorithms that resort to complex in-studio
motion capture solutions, and thereby train more expressive synthesis
algorithms. Our experiments and user study show the state-of-the-art quality of
our speech-synthesized full 3D character animations
Learning Speech-driven {3D} Conversational Gestures from Video
We propose the first approach to automatically and jointly synthesize both the synchronous 3D conversational body and hand gestures, as well as 3D face and head animations, of a virtual character from speech input. Our algorithm uses a CNN architecture that leverages the inherent correlation between facial expression and hand gestures. Synthesis of conversational body gestures is a multi-modal problem since many similar gestures can plausibly accompany the same input speech. To synthesize plausible body gestures in this setting, we train a Generative Adversarial Network (GAN) based model that measures the plausibility of the generated sequences of 3D body motion when paired with the input audio features. 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. To this end, we apply state-of-the-art monocular approaches for 3D body and hand pose estimation as well as dense 3D face performance capture to the video corpus. In this way, we can train on orders of magnitude more data than previous algorithms that resort to complex in-studio motion capture solutions, and thereby train more expressive synthesis algorithms. Our experiments and user study show the state-of-the-art quality of our speech-synthesized full 3D character animations
Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations
Expressive text-to-speech (TTS) aims to synthesize speeches with human-like
tones, moods, or even artistic attributes. Recent advancements in expressive
TTS empower users with the ability to directly control synthesis style through
natural language prompts. However, these methods often require excessive
training with a significant amount of style-annotated data, which can be
challenging to acquire. Moreover, they may have limited adaptability due to
fixed style annotations. In this work, we present FreeStyleTTS (FS-TTS), a
controllable expressive TTS model with minimal human annotations. Our approach
utilizes a large language model (LLM) to transform expressive TTS into a style
retrieval task. The LLM selects the best-matching style references from
annotated utterances based on external style prompts, which can be raw input
text or natural language style descriptions. The selected reference guides the
TTS pipeline to synthesize speeches with the intended style. This innovative
approach provides flexible, versatile, and precise style control with minimal
human workload. Experiments on a Mandarin storytelling corpus demonstrate
FS-TTS's proficiency in leveraging LLM's semantic inference ability to retrieve
desired styles from either input text or user-defined descriptions. This
results in synthetic speeches that are closely aligned with the specified
styles.Comment: 5 pages,3 figures, submitted to ICASSP 202
Mandarin Singing Voice Synthesis Based on Harmonic Plus Noise Model and Singing Expression Analysis
The purpose of this study is to investigate how humans interpret musical
scores expressively, and then design machines that sing like humans. We
consider six factors that have a strong influence on the expression of human
singing. The factors are related to the acoustic, phonetic, and musical
features of a real singing signal. Given real singing voices recorded following
the MIDI scores and lyrics, our analysis module can extract the expression
parameters from the real singing signals semi-automatically. The expression
parameters are used to control the singing voice synthesis (SVS) system for
Mandarin Chinese, which is based on the harmonic plus noise model (HNM). The
results of perceptual experiments show that integrating the expression factors
into the SVS system yields a notable improvement in perceptual naturalness,
clearness, and expressiveness. By one-to-one mapping of the real singing signal
and expression controls to the synthesizer, our SVS system can simulate the
interpretation of a real singer with the timbre of a speaker.Comment: 8 pages, technical repor
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