Text2Video: Text-driven Talking-head Video Synthesis with Phonetic Dictionary

• URL: http://arxiv.org/abs/2104.14631v1
• Date: Thu, 29 Apr 2021 19:54:41 GMT
• Title: Text2Video: Text-driven Talking-head Video Synthesis with Phonetic Dictionary
• Authors: Sibo Zhang, Jiahong Yuan, Miao Liao, Liangjun Zhang
• Abstract summary: We present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video. Compared to audio-driven video generation algorithms, our approach has a number of advantages.
• Score: 10.590649169151055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.

Related papers

• Towards Accurate Lip-to-Speech Synthesis in-the-Wild [31.289366690147556]
  We introduce a novel approach to address the task of synthesizing speech from silent videos of any in-the-wild speaker solely based on lip movements. The traditional approach of directly generating speech from lip videos faces the challenge of not being able to learn a robust language model from speech alone. We propose incorporating noisy text supervision using a state-of-the-art lip-to-text network that instills language information into our model.
  arXiv  Detail & Related papers  (2024-03-02T04:07:24Z)
• Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism [26.180371869137257]
  State of the art in talking face generation focuses mainly on lip-syncing, being conditioned on audio clips. NEUral Text to ARticulate Talk (NEUTART) is a talking face generator that uses a joint audiovisual feature space. Model produces photorealistic talking face videos with human-like articulation and well-synced audiovisual streams.
  arXiv  Detail & Related papers  (2023-12-11T18:41:55Z)
• Text-to-Video: a Two-stage Framework for Zero-shot Identity-agnostic Talking-head Generation [16.12424393291571]
  We propose a novel two-stage framework for person-agnostic video cloning. In the first stage, we leverage pretrained zero-shot models to achieve text-to-speech conversion. In the second stage, an audio-driven talking head generation method is employed to produce compelling videos.
  arXiv  Detail & Related papers  (2023-08-12T03:30:49Z)
• Exploring the Role of Audio in Video Captioning [59.679122191706426]
  We present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. We propose new local-global fusion mechanisms to improve information exchange across audio and video.
  arXiv  Detail & Related papers  (2023-06-21T20:54:52Z)
• CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models [50.42886595228255]
  We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge. We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining model.
  arXiv  Detail & Related papers  (2023-06-16T05:42:01Z)
• Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval [70.30052749168013]
  Multi-channel video-language retrieval require models to understand information from different channels. contrastive multimodal models are shown to be highly effective at aligning entities in images/videos and text. There is not a clear way to quickly adapt these two lines to multi-channel video-language retrieval with limited data and resources.
  arXiv  Detail & Related papers  (2022-06-05T01:43:52Z)
• SVTS: Scalable Video-to-Speech Synthesis [105.29009019733803]
  We introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder. We are the first to show intelligible results on the challenging LRS3 dataset.
  arXiv  Detail & Related papers  (2022-05-04T13:34:07Z)
• End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks [54.43697805589634]
  We propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) Our model consists of an encoder-decoder architecture that receives raw video as input and generates speech. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID.
  arXiv  Detail & Related papers  (2021-04-27T17:12:30Z)
• TEACHTEXT: CrossModal Generalized Distillation for Text-Video Retrieval [103.85002875155551]
  We propose a novel generalized distillation method, TeachText, for exploiting large-scale language pretraining. We extend our method to video side modalities and show that we can effectively reduce the number of used modalities at test time. Our approach advances the state of the art on several video retrieval benchmarks by a significant margin and adds no computational overhead at test time.
  arXiv  Detail & Related papers  (2021-04-16T17:55:28Z)
• 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.