StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads

• URL: http://arxiv.org/abs/2409.09292v1
• Date: Sat, 14 Sep 2024 03:49:38 GMT
• Title: StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads
• Authors: Suzhen Wang, Yifeng Ma, Yu Ding, Zhipeng Hu, Changjie Fan, Tangjie Lv, Zhidong Deng, Xin Yu,
• Abstract summary: Existing one-shot talking head methods fail to produce diverse speaking styles in the final videos. We propose a one-shot style-controllable talking face generation method that can obtain speaking styles from reference videos. Our method generates visually authentic talking head videos with diverse speaking styles from only one portrait image and an audio clip.
• Score: 46.749597670092484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Individuals have unique facial expression and head pose styles that reflect their personalized speaking styles. Existing one-shot talking head methods cannot capture such personalized characteristics and therefore fail to produce diverse speaking styles in the final videos. To address this challenge, we propose a one-shot style-controllable talking face generation method that can obtain speaking styles from reference speaking videos and drive the one-shot portrait to speak with the reference speaking styles and another piece of audio. Our method aims to synthesize the style-controllable coefficients of a 3D Morphable Model (3DMM), including facial expressions and head movements, in a unified framework. Specifically, the proposed framework first leverages a style encoder to extract the desired speaking styles from the reference videos and transform them into style codes. Then, the framework uses a style-aware decoder to synthesize the coefficients of 3DMM from the audio input and style codes. During decoding, our framework adopts a two-branch architecture, which generates the stylized facial expression coefficients and stylized head movement coefficients, respectively. After obtaining the coefficients of 3DMM, an image renderer renders the expression coefficients into a specific person's talking-head video. Extensive experiments demonstrate that our method generates visually authentic talking head videos with diverse speaking styles from only one portrait image and an audio clip.

Related papers

• MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes [74.82911268630463]
  Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. MimicTalk exploits the rich knowledge from a NeRF-based person-agnostic generic model for improving the efficiency and robustness of personalized TFG. Experiments show that our MimicTalk surpasses previous baselines regarding video quality, efficiency, and expressiveness.
  arXiv  Detail & Related papers  (2024-10-09T10:12:37Z)
• Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation [41.489700112318864]
  Speech-driven 3D facial animation aims to synthesize vivid facial animations that accurately synchronize with speech and match the unique speaking style. We introduce an innovative speaking style disentanglement method, which enables arbitrary-subject speaking style encoding. We also propose a novel framework called textbfMimic to learn disentangled representations of the speaking style and content from facial motions.
  arXiv  Detail & Related papers  (2023-12-18T01:49:42Z)
• Personalized Speech-driven Expressive 3D Facial Animation Synthesis with Style Control [1.8540152959438578]
  A realistic facial animation system should consider such identity-specific speaking styles and facial idiosyncrasies to achieve high-degree of naturalness and plausibility. We present a speech-driven expressive 3D facial animation synthesis framework that models identity specific facial motion as latent representations (called as styles) Our framework is trained in an end-to-end fashion and has a non-autoregressive encoder-decoder architecture with three main components.
  arXiv  Detail & Related papers  (2023-10-25T21:22:28Z)
• AdaMesh: Personalized Facial Expressions and Head Poses for Adaptive Speech-Driven 3D Facial Animation [49.4220768835379]
  AdaMesh is a novel adaptive speech-driven facial animation approach. It learns the personalized talking style from a reference video of about 10 seconds. It generates vivid facial expressions and head poses.
  arXiv  Detail & Related papers  (2023-10-11T06:56:08Z)
• DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models [24.401443462720135]
  We propose DiffPoseTalk, a generative framework based on the diffusion model combined with a style encoder. In particular, our style includes the generation of head poses, thereby enhancing user perception. We address the shortage of scanned 3D talking face data by training our model on reconstructed 3DMM parameters from a high-quality, in-the-wild audio-visual dataset.
  arXiv  Detail & Related papers  (2023-09-30T17:01:18Z)
• Visual Captioning at Will: Describing Images and Videos Guided by a Few Stylized Sentences [49.66987347397398]
  Few-Shot Stylized Visual Captioning aims to generate captions in any desired style, using only a few examples as guidance during inference. We propose a framework called FS-StyleCap for this task, which utilizes a conditional encoder-decoder language model and a visual projection module.
  arXiv  Detail & Related papers  (2023-07-31T04:26:01Z)
• StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles [43.12918949398099]
  We propose a one-shot style-controllable talking face generation framework. We aim to attain a speaking style from an arbitrary reference speaking video. We then drive the one-shot portrait to speak with the reference speaking style and another piece of audio.
  arXiv  Detail & Related papers  (2023-01-03T13:16:24Z)
• Imitating Arbitrary Talking Style for Realistic Audio-DrivenTalking Face Synthesis [17.650661515807993]
  We propose to inject style into the talking face synthesis framework through imitating arbitrary talking style of the particular reference video. We devise a latent-style-fusion(LSF) model to synthesize stylized talking faces by imitating talking styles from the style codes.
  arXiv  Detail & Related papers  (2021-10-30T08:15:27Z)
• MakeItTalk: Speaker-Aware Talking-Head Animation [49.77977246535329]
  We present a method that generates expressive talking heads from a single facial image with audio as the only input. Based on this intermediate representation, our method is able to synthesize photorealistic videos of entire talking heads with full range of motion.
  arXiv  Detail & Related papers  (2020-04-27T17:56:15Z)

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.