A Novel Speech-Driven Lip-Sync Model with CNN and LSTM

• URL: http://arxiv.org/abs/2205.00916v1
• Date: Mon, 2 May 2022 13:57:50 GMT
• Title: A Novel Speech-Driven Lip-Sync Model with CNN and LSTM
• Authors: Xiaohong Li, Xiang Wang, Kai Wang, Shiguo Lian
• Abstract summary: We present a combined deep neural network of one-dimensional convolutions and LSTM to generate displacement of a 3D template face model from variable-length speech input. In order to enhance the robustness of the network to different sound signals, we adapt a trained speech recognition model to extract speech feature. We show that our model is able to generate smooth and natural lip movements synchronized with speech.
• Score: 12.747541089354538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters. In this paper, we present a combined deep neural network of one-dimensional convolutions and LSTM to generate vertex displacement of a 3D template face model from variable-length speech input. The motion of the lower part of the face, which is represented by the vertex movement of 3D lip shapes, is consistent with the input speech. In order to enhance the robustness of the network to different sound signals, we adapt a trained speech recognition model to extract speech feature, and a velocity loss term is adopted to reduce the jitter of generated facial animation. We recorded a series of videos of a Chinese adult speaking Mandarin and created a new speech-animation dataset to compensate the lack of such public data. Qualitative and quantitative evaluations indicate that our model is able to generate smooth and natural lip movements synchronized with speech.

Related papers

• SAiD: Speech-driven Blendshape Facial Animation with Diffusion [6.4271091365094515]
  Speech-driven 3D facial animation is challenging due to the scarcity of large-scale visual-audio datasets. We propose a speech-driven 3D facial animation with a diffusion model (SAiD), a lightweight Transformer-based U-Net with a cross-modality alignment bias between audio and visual to enhance lip synchronization.
  arXiv  Detail & Related papers  (2023-12-25T04:40:32Z)
• 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)
• GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance [83.43852715997596]
  GSmoothFace is a novel two-stage generalized talking face generation model guided by a fine-grained 3d face model. It can synthesize smooth lip dynamics while preserving the speaker's identity. Both quantitative and qualitative experiments confirm the superiority of our method in terms of realism, lip synchronization, and visual quality.
  arXiv  Detail & Related papers  (2023-12-12T16:00:55Z)
• Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video [91.92782707888618]
  We present a decomposition-composition framework named Speech to Lip (Speech2Lip) that disentangles speech-sensitive and speech-insensitive motion/appearance. We show that our model can be trained by a video of just a few minutes in length and achieve state-of-the-art performance in both visual quality and speech-visual synchronization.
  arXiv  Detail & Related papers  (2023-09-09T14:52:39Z)
• DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with Diffusion [68.85904927374165]
  We propose DF-3DFace, a diffusion-driven speech-to-3D face mesh synthesis. It captures the complex one-to-many relationships between speech and 3D face based on diffusion. It simultaneously achieves more realistic facial animation than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-23T04:14:55Z)
• Generating Holistic 3D Human Motion from Speech [97.11392166257791]
  We build a high-quality dataset of 3D holistic body meshes with synchronous speech. We then define a novel speech-to-motion generation framework in which the face, body, and hands are modeled separately.
  arXiv  Detail & Related papers  (2022-12-08T17:25:19Z)
• 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)
• Speech2Video Synthesis with 3D Skeleton Regularization and Expressive Body Poses [36.00309828380724]
  We propose a novel approach to convert given speech audio to a photo-realistic speaking video of a specific person. We achieve this by first generating 3D skeleton movements from the audio sequence using a recurrent neural network (RNN) To make the skeleton movement realistic and expressive, we embed the knowledge of an articulated 3D human skeleton and a learned dictionary of personal speech iconic gestures into the generation process.
  arXiv  Detail & Related papers  (2020-07-17T19:30:14Z)

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.