PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based Talking Head Synthesis

• URL: http://arxiv.org/abs/2412.08504v1
• Date: Wed, 11 Dec 2024 16:15:14 GMT
• Title: PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based Talking Head Synthesis
• Authors: Yifan Xie, Tao Feng, Xin Zhang, Xiangyang Luo, Zixuan Guo, Weijiang Yu, Heng Chang, Fei Ma, Fei Richard Yu,
• Abstract summary: Methods based on radiance fields have received increasing attention due to their ability to synthesize high-fidelity talking heads. We propose a novel 3D Gaussian-based method called PointTalk, which constructs a static 3D Gaussian field of the head and deforms it in sync with the audio. Our method achieves superior high-fidelity and audio-lip synchronization in talking head synthesis compared to previous methods.
• Score: 27.97031664678664
• License:
• Abstract: Talking head synthesis with arbitrary speech audio is a crucial challenge in the field of digital humans. Recently, methods based on radiance fields have received increasing attention due to their ability to synthesize high-fidelity and identity-consistent talking heads from just a few minutes of training video. However, due to the limited scale of the training data, these methods often exhibit poor performance in audio-lip synchronization and visual quality. In this paper, we propose a novel 3D Gaussian-based method called PointTalk, which constructs a static 3D Gaussian field of the head and deforms it in sync with the audio. It also incorporates an audio-driven dynamic lip point cloud as a critical component of the conditional information, thereby facilitating the effective synthesis of talking heads. Specifically, the initial step involves generating the corresponding lip point cloud from the audio signal and capturing its topological structure. The design of the dynamic difference encoder aims to capture the subtle nuances inherent in dynamic lip movements more effectively. Furthermore, we integrate the audio-point enhancement module, which not only ensures the synchronization of the audio signal with the corresponding lip point cloud within the feature space, but also facilitates a deeper understanding of the interrelations among cross-modal conditional features. Extensive experiments demonstrate that our method achieves superior high-fidelity and audio-lip synchronization in talking head synthesis compared to previous methods.

Related papers

• SayAnything: Audio-Driven Lip Synchronization with Conditional Video Diffusion [78.77211425667542]
  SayAnything is a conditional video diffusion framework that directly synthesizes lip movements from audio input. Our novel design effectively balances different condition signals in the latent space, enabling precise control over appearance, motion, and region-specific generation.
  arXiv  Detail & Related papers  (2025-02-17T07:29:36Z)
• S^3D-NeRF: Single-Shot Speech-Driven Neural Radiance Field for High Fidelity Talking Head Synthesis [14.437741528053504]
  We design a Single-Shot Speech-Driven Radiance Field (S3D-NeRF) method to tackle the three difficulties: learning a representative appearance feature for each identity, modeling motion of different face regions with audio, and keeping the temporal consistency of the lip area. Our S3D-NeRF surpasses previous arts on both video fidelity and audio-lip synchronization.
  arXiv  Detail & Related papers  (2024-08-18T03:59:57Z)
• FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio [45.71036380866305]
  We abstract the process of people hearing speech, extracting meaningful cues, and creating dynamically audio-consistent talking faces from a single audio. Specifically, it involves two critical challenges: one is to effectively decouple identity, content, and emotion from entangled audio, and the other is to maintain intra-video diversity and inter-video consistency. We introduce the Controllable Coherent Frame generation, which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models.
  arXiv  Detail & Related papers  (2024-03-04T09:59:48Z)
• 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)
• DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation [78.08004432704826]
  We model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk) In this paper, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. Our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost.
  arXiv  Detail & Related papers  (2023-01-10T05:11:25Z)
• StyleTalker: One-shot Style-based Audio-driven Talking Head Video Generation [47.06075725469252]
  StyleTalker is an audio-driven talking head generation model. It can synthesize a video of a talking person from a single reference image. Our model is able to synthesize talking head videos with impressive perceptual quality.
  arXiv  Detail & Related papers  (2022-08-23T12:49:01Z)
• SVTS: Scalable Video-to-Speech Synthesis [105.29009019733803]
  We introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder. We are the first to show intelligible results on the challenging LRS3 dataset.
  arXiv  Detail & Related papers  (2022-05-04T13:34:07Z)
• DFA-NeRF: Personalized Talking Head Generation via Disentangled Face Attributes Neural Rendering [69.9557427451339]
  We propose a framework based on neural radiance field to pursue high-fidelity talking head generation. Specifically, neural radiance field takes lip movements features and personalized attributes as two disentangled conditions. We show that our method achieves significantly better results than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-03T18:23:38Z)
• AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis [55.24336227884039]
  We present a novel framework to generate high-fidelity talking head video. We use neural scene representation networks to bridge the gap between audio input and video output. Our framework can (1) produce high-fidelity and natural results, and (2) support free adjustment of audio signals, viewing directions, and background images.
  arXiv  Detail & Related papers  (2021-03-20T02:58:13Z)

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.