Audio- and Gaze-driven Facial Animation of Codec Avatars

• URL: http://arxiv.org/abs/2008.05023v1
• Date: Tue, 11 Aug 2020 22:28:48 GMT
• Title: Audio- and Gaze-driven Facial Animation of Codec Avatars
• Authors: Alexander Richard, Colin Lea, Shugao Ma, Juergen Gall, Fernando de la Torre, Yaser Sheikh
• Abstract summary: 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.
• Score: 149.0094713268313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Codec Avatars are a recent class of learned, photorealistic face models that accurately represent the geometry and texture of a person in 3D (i.e., for virtual reality), and are almost indistinguishable from video. In this paper we describe the first approach to animate these parametric models in real-time which could be deployed on commodity virtual reality hardware using audio and/or eye tracking. Our goal is to display expressive conversations between individuals that exhibit important social signals such as laughter and excitement solely from latent cues in our lossy input signals. To this end we collected over 5 hours of high frame rate 3D face scans across three participants including traditional neutral speech as well as expressive and conversational speech. We investigate a multimodal fusion approach that dynamically identifies which sensor encoding should animate which parts of the face at any time. See the supplemental video which demonstrates our ability to generate full face motion far beyond the typically neutral lip articulations seen in competing work: https://research.fb.com/videos/audio-and-gaze-driven-facial-animation-of-codec-avatars/

Related papers

• EmoFace: Audio-driven Emotional 3D Face Animation [3.573880705052592]
  EmoFace is a novel audio-driven methodology for creating facial animations with vivid emotional dynamics. Our approach can generate facial expressions with multiple emotions, and has the ability to generate random yet natural blinks and eye movements. Our proposed methodology can be applied in producing dialogues animations of non-playable characters in video games, and driving avatars in virtual reality environments.
  arXiv  Detail & Related papers  (2024-07-17T11:32:16Z)
• Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance [41.692420421029695]
  We introduce an efficient variational auto-encoder mapping facial geometry and images to a highly generalized expression latent space. We then use GNPFA to extract high-quality expressions and accurate head poses from a large array of videos. We propose Media2Face, a diffusion model in GNPFA latent space for co-speech facial animation generation.
  arXiv  Detail & Related papers  (2024-01-28T16:17:59Z)
• 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)
• 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)
• FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation Synthesis Using Self-Supervised Speech Representation Learning [0.0]
  FaceXHuBERT is a text-less speech-driven 3D facial animation generation method. It is very robust to background noise and can handle audio recorded in a variety of situations. It produces superior results with respect to the realism of the animation 78% of the time.
  arXiv  Detail & Related papers  (2023-03-09T17:05:19Z)
• FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning [23.14865405847467]
  We propose a talking face generation method that takes an audio signal as input and a short target video clip as reference. The method synthesizes a photo-realistic video of the target face with natural lip motions, head poses, and eye blinks that are in-sync with the input audio signal. Experimental results and user studies show our method can generate realistic talking face videos with better qualities than the results of state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-18T02:10:26Z)
• MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement [142.9900055577252]
  We propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face. Our approach ensures highly accurate lip motion, while also plausible animation of the parts of the face that are uncorrelated to the audio signal, such as eye blinks and eye brow motion.
  arXiv  Detail & Related papers  (2021-04-16T17:05:40Z)
• 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-driven Talking Face Video Generation with Learning-based Personalized Head Pose [67.31838207805573]
  We propose a deep neural network model that takes an audio signal A of a source person and a short video V of a target person as input. We outputs a synthesized high-quality talking face video with personalized head pose. Our method can generate high-quality talking face videos with more distinguishing head movement effects than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-24T10:02:10Z)

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.