DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation
- URL: http://arxiv.org/abs/2401.04747v2
- Date: Sat, 6 Apr 2024 14:53:51 GMT
- Title: DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation
- Authors: Junming Chen, Yunfei Liu, Jianan Wang, Ailing Zeng, Yu Li, Qifeng Chen,
- Abstract summary: DiffSHEG is a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation with arbitrary length.
By enabling the real-time generation of expressive and synchronized motions, DiffSHEG showcases its potential for various applications in the development of digital humans and embodied agents.
- Score: 72.85685916829321
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We propose DiffSHEG, a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation with arbitrary length. While previous works focused on co-speech gesture or expression generation individually, the joint generation of synchronized expressions and gestures remains barely explored. To address this, our diffusion-based co-speech motion generation transformer enables uni-directional information flow from expression to gesture, facilitating improved matching of joint expression-gesture distributions. Furthermore, we introduce an outpainting-based sampling strategy for arbitrary long sequence generation in diffusion models, offering flexibility and computational efficiency. Our method provides a practical solution that produces high-quality synchronized expression and gesture generation driven by speech. Evaluated on two public datasets, our approach achieves state-of-the-art performance both quantitatively and qualitatively. Additionally, a user study confirms the superiority of DiffSHEG over prior approaches. By enabling the real-time generation of expressive and synchronized motions, DiffSHEG showcases its potential for various applications in the development of digital humans and embodied agents.
Related papers
- OrientDream: Streamlining Text-to-3D Generation with Explicit Orientation Control [66.03885917320189]
OrientDream is a camera orientation conditioned framework for efficient and multi-view consistent 3D generation from textual prompts.
Our strategy emphasizes the implementation of an explicit camera orientation conditioned feature in the pre-training of a 2D text-to-image diffusion module.
Our experiments reveal that our method not only produces high-quality NeRF models with consistent multi-view properties but also achieves an optimization speed significantly greater than existing methods.
arXiv Detail & Related papers (2024-06-14T13:16:18Z) - Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI)
In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion)
Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
arXiv Detail & Related papers (2024-02-15T18:59:18Z) - FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models [85.16273912625022]
We introduce FaceTalk, a novel generative approach designed for synthesizing high-fidelity 3D motion sequences of talking human heads from audio signal.
To the best of our knowledge, this is the first work to propose a generative approach for realistic and high-quality motion synthesis of human heads.
arXiv Detail & Related papers (2023-12-13T19:01:07Z) - Investigating the Design Space of Diffusion Models for Speech Enhancement [17.914763947871368]
Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature.
We show that the performance of previous diffusion-based speech enhancement systems cannot be attributed to the progressive transformation between the clean and noisy speech signals.
We also show that a proper choice of preconditioning, training loss weighting, SDE and sampler allows to outperform a popular diffusion-based speech enhancement system.
arXiv Detail & Related papers (2023-12-07T15:40:55Z) - Realistic Speech-to-Face Generation with Speech-Conditioned Latent
Diffusion Model with Face Prior [13.198105709331617]
We propose a novel speech-to-face generation framework, which leverages a Speech-Conditioned Latent Diffusion Model, called SCLDM.
This is the first work to harness the exceptional modeling capabilities of diffusion models for speech-to-face generation.
We show that our method can produce more realistic face images while preserving the identity of the speaker better than state-of-the-art methods.
arXiv Detail & Related papers (2023-10-05T07:44:49Z) - Multimodal-driven Talking Face Generation via a Unified Diffusion-based
Generator [29.58245990622227]
Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio.
Existing methods ignore the potential of text modal, and their generators mainly follow the source-oriented feature paradigm coupled with unstable GAN frameworks.
We derive a novel paradigm free of unstable seesaw-style optimization, resulting in simple, stable, and effective training and inference schemes.
arXiv Detail & Related papers (2023-05-04T07:01:36Z) - AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech
Gesture Synthesis [0.0]
We present an approach to pre-train partial gesture sequences using a generative adversarial network with a quantization pipeline.
By learning the mapping of a latent space representation as opposed to directly mapping it to a vector representation, this framework facilitates the generation of highly realistic and expressive gestures.
arXiv Detail & Related papers (2023-05-02T07:59:38Z) - DiffVoice: Text-to-Speech with Latent Diffusion [18.150627638754923]
We present DiffVoice, a novel text-to-speech model based on latent diffusion.
Subjective evaluations on LJSpeech and LibriTTS datasets demonstrate that our method beats the best publicly available systems in naturalness.
arXiv Detail & Related papers (2023-04-23T21:05:33Z) - Unified Discrete Diffusion for Simultaneous Vision-Language Generation [78.21352271140472]
We present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks.
Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix.
Our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.
arXiv Detail & Related papers (2022-11-27T14:46:01Z) - Conditional Diffusion Probabilistic Model for Speech Enhancement [101.4893074984667]
We propose a novel speech enhancement algorithm that incorporates characteristics of the observed noisy speech signal into the diffusion and reverse processes.
In our experiments, we demonstrate strong performance of the proposed approach compared to representative generative models.
arXiv Detail & Related papers (2022-02-10T18:58:01Z)
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.