EmoTalk3D: High-Fidelity Free-View Synthesis of Emotional 3D Talking Head

• URL: http://arxiv.org/abs/2408.00297v1
• Date: Thu, 1 Aug 2024 05:46:57 GMT
• Title: EmoTalk3D: High-Fidelity Free-View Synthesis of Emotional 3D Talking Head
• Authors: Qianyun He, Xinya Ji, Yicheng Gong, Yuanxun Lu, Zhengyu Diao, Linjia Huang, Yao Yao, Siyu Zhu, Zhan Ma, Songcen Xu, Xiaofei Wu, Zixiao Zhang, Xun Cao, Hao Zhu,
• Abstract summary: We present a novel approach for synthesizing 3D talking heads with controllable emotion. Our model enables controllable emotion in the generated talking heads and can be rendered in wide-range views. Experiments demonstrate the effectiveness of our approach in generating high-fidelity and emotion-controllable 3D talking heads.
• Score: 30.138347111341748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach for synthesizing 3D talking heads with controllable emotion, featuring enhanced lip synchronization and rendering quality. Despite significant progress in the field, prior methods still suffer from multi-view consistency and a lack of emotional expressiveness. To address these issues, we collect EmoTalk3D dataset with calibrated multi-view videos, emotional annotations, and per-frame 3D geometry. By training on the EmoTalk3D dataset, we propose a \textit{`Speech-to-Geometry-to-Appearance'} mapping framework that first predicts faithful 3D geometry sequence from the audio features, then the appearance of a 3D talking head represented by 4D Gaussians is synthesized from the predicted geometry. The appearance is further disentangled into canonical and dynamic Gaussians, learned from multi-view videos, and fused to render free-view talking head animation. Moreover, our model enables controllable emotion in the generated talking heads and can be rendered in wide-range views. Our method exhibits improved rendering quality and stability in lip motion generation while capturing dynamic facial details such as wrinkles and subtle expressions. Experiments demonstrate the effectiveness of our approach in generating high-fidelity and emotion-controllable 3D talking heads. The code and EmoTalk3D dataset are released at https://nju-3dv.github.io/projects/EmoTalk3D.

Related papers

• MMHead: Towards Fine-grained Multi-modal 3D Facial Animation [68.04052669266174]
  We construct a large-scale multi-modal 3D facial animation dataset, MMHead. MMHead consists of 49 hours of 3D facial motion sequences, speech audios, and rich hierarchical text annotations. Based on the MMHead dataset, we establish benchmarks for two new tasks: text-induced 3D talking head animation and text-to-3D facial motion generation.
  arXiv  Detail & Related papers  (2024-10-10T09:37:01Z)
• Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description [3.52270271101496]
  "Emo3D" is an extensive "Text-Image-Expression dataset" spanning a wide spectrum of human emotions. We generate a diverse array of textual descriptions, facilitating the capture of a broad spectrum of emotional expressions. "Emo3D" has great applications in animation design, virtual reality, and emotional human-computer interaction.
  arXiv  Detail & Related papers  (2024-10-02T21:31:24Z)
• EmoVOCA: Speech-Driven Emotional 3D Talking Heads [12.161006152509653]
  We propose an innovative data-driven technique for creating a synthetic dataset, called EmoVOCA. We then designed and trained an emotional 3D talking head generator that accepts a 3D face, an audio file, an emotion label, and an intensity value as inputs, and learns to animate the audio-synchronized lip movements with expressive traits of the face.
  arXiv  Detail & Related papers  (2024-03-19T16:33:26Z)
• Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis [88.17520303867099]
  One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio. We present Real3D-Potrait, a framework that improves the one-shot 3D reconstruction power with a large image-to-plane model. Experiments show that Real3D-Portrait generalizes well to unseen identities and generates more realistic talking portrait videos.
  arXiv  Detail & Related papers  (2024-01-16T17:04:30Z)
• PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features [22.31865247379668]
  Speech-driven 3D facial animation has improved a lot recently. Most related works only utilize acoustic modality and neglect the influence of visual and textual cues. We present a novel framework, namely PMMTalk, using complementary Pseudo Multi-Modal features for improving the accuracy of facial animation.
  arXiv  Detail & Related papers  (2023-12-05T14:12:38Z)
• TADA! Text to Animatable Digital Avatars [57.52707683788961]
  TADA takes textual descriptions and produces expressive 3D avatars with high-quality geometry and lifelike textures. We derive an optimizable high-resolution body model from SMPL-X with 3D displacements and a texture map. We render normals and RGB images of the generated character and exploit their latent embeddings in the SDS training process.
  arXiv  Detail & Related papers  (2023-08-21T17:59:10Z)
• SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation [33.651156455111916]
  We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio. Precisely, we present ExpNet to learn the accurate facial expression from audio by distilling both coefficients and 3D-rendered faces.
  arXiv  Detail & Related papers  (2022-11-22T11:35:07Z)
• EMOCA: Emotion Driven Monocular Face Capture and Animation [59.15004328155593]
  We introduce a novel deep perceptual emotion consistency loss during training, which helps ensure that the reconstructed 3D expression matches the expression depicted in the input image. On the task of in-the-wild emotion recognition, our purely geometric approach is on par with the best image-based methods, highlighting the value of 3D geometry in analyzing human behavior.
  arXiv  Detail & Related papers  (2022-04-24T15:58:35Z)
• 3D-TalkEmo: Learning to Synthesize 3D Emotional Talking Head [13.305263646852087]
  We introduce 3D-TalkEmo, a deep neural network that generates 3D talking head animation with various emotions. We also create a large 3D dataset with synchronized audios and videos, rich corpus, as well as various emotion states of different persons.
  arXiv  Detail & Related papers  (2021-04-25T02:48:19Z)
• 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)
• Audio- and Gaze-driven Facial Animation of Codec Avatars [149.0094713268313]
  We describe the first approach to animate Codec Avatars in real-time using audio and/or eye tracking. Our goal is to display expressive conversations between individuals that exhibit important social signals.
  arXiv  Detail & Related papers  (2020-08-11T22:28:48Z)

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.