AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding

• URL: http://arxiv.org/abs/2405.03121v1
• Date: Mon, 6 May 2024 02:32:41 GMT
• Title: AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding
• Authors: Tao Liu, Feilong Chen, Shuai Fan, Chenpeng Du, Qi Chen, Xie Chen, Kai Yu,
• Abstract summary: The paper introduces AniTalker, a framework designed to generate lifelike talking faces from a single portrait. AniTalker effectively captures a wide range of facial dynamics, including subtle expressions and head movements.
• Score: 24.486705010561067
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait. Unlike existing models that primarily focus on verbal cues such as lip synchronization and fail to capture the complex dynamics of facial expressions and nonverbal cues, AniTalker employs a universal motion representation. This innovative representation effectively captures a wide range of facial dynamics, including subtle expressions and head movements. AniTalker enhances motion depiction through two self-supervised learning strategies: the first involves reconstructing target video frames from source frames within the same identity to learn subtle motion representations, and the second develops an identity encoder using metric learning while actively minimizing mutual information between the identity and motion encoders. This approach ensures that the motion representation is dynamic and devoid of identity-specific details, significantly reducing the need for labeled data. Additionally, the integration of a diffusion model with a variance adapter allows for the generation of diverse and controllable facial animations. This method not only demonstrates AniTalker's capability to create detailed and realistic facial movements but also underscores its potential in crafting dynamic avatars for real-world applications. Synthetic results can be viewed at https://github.com/X-LANCE/AniTalker.

Related papers

• X-Dyna: Expressive Dynamic Human Image Animation [49.896933584815926]
  X-Dyna is a zero-shot, diffusion-based pipeline for animating a single human image. It generates realistic, context-aware dynamics for both the subject and the surrounding environment.
  arXiv  Detail & Related papers  (2025-01-17T08:10:53Z)
• GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expression [33.886734972316326]
  GoHD is a framework designed to produce highly realistic, expressive, and controllable portrait videos from any reference identity with any motion. An animation module utilizing latent navigation is introduced to improve the generalization ability across unseen input styles. A conformer-structured conditional diffusion model is designed to guarantee head poses that are aware of prosody. A two-stage training strategy is devised to decouple frequent and frame-wise lip motion distillation from the generation of other more temporally dependent but less audio-related motions.
  arXiv  Detail & Related papers  (2024-12-12T14:12:07Z)
• 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)
• 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)
• From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations [107.88375243135579]
  Given speech audio, we output multiple possibilities of gestural motion for an individual, including face, body, and hands. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures. Experiments show our model generates appropriate and diverse gestures, outperforming both diffusion- and VQ-only methods.
  arXiv  Detail & Related papers  (2024-01-03T18:55:16Z)
• 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)
• Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations [61.65012981435094]
  DIRFA is a novel method that can generate talking faces with diverse yet realistic facial animations from the same driving audio. To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network. We show that DIRFA can generate talking faces with realistic facial animations effectively.
  arXiv  Detail & Related papers  (2023-04-18T12:36:15Z)
• That's What I Said: Fully-Controllable Talking Face Generation [16.570649208028343]
  We propose a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only represents motion-related features while eliminating identity information. Our method can generate natural-looking talking faces with fully controllable facial attributes and accurate lip synchronisation.
  arXiv  Detail & Related papers  (2023-04-06T17:56:50Z)
• Imitator: Personalized Speech-driven 3D Facial Animation [63.57811510502906]
  State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor. We present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video. We show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.
  arXiv  Detail & Related papers  (2022-12-30T19:00:02Z)
• Language-Guided Face Animation by Recurrent StyleGAN-based Generator [87.56260982475564]
  We study a novel task, language-guided face animation, that aims to animate a static face image with the help of languages. We propose a recurrent motion generator to extract a series of semantic and motion information from the language and feed it along with visual information to a pre-trained StyleGAN to generate high-quality frames.
  arXiv  Detail & Related papers  (2022-08-11T02:57:30Z)

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.