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Talking-head Generation with Rhythmic Head Motion (2007.08547v1)

Published 16 Jul 2020 in cs.CV and cs.GR

Abstract: When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information. However, generating a lip-synced video while moving head naturally is challenging. While remarkably successful, existing works either generate still talkingface videos or rely on landmark/video frames as sparse/dense mapping guidance to generate head movements, which leads to unrealistic or uncontrollable video synthesis. To overcome the limitations, we propose a 3D-aware generative network along with a hybrid embedding module and a non-linear composition module. Through modeling the head motion and facial expressions1 explicitly, manipulating 3D animation carefully, and embedding reference images dynamically, our approach achieves controllable, photo-realistic, and temporally coherent talking-head videos with natural head movements. Thoughtful experiments on several standard benchmarks demonstrate that our method achieves significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available on https://github.com/ lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion.

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Authors (7)
  1. Lele Chen (22 papers)
  2. Guofeng Cui (7 papers)
  3. Celong Liu (7 papers)
  4. Zhong Li (53 papers)
  5. Ziyi Kou (14 papers)
  6. Yi Xu (304 papers)
  7. Chenliang Xu (114 papers)
Citations (170)
View on Semantic Scholar

Summary

Talking-head Generation with Rhythmic Head Motion

The paper "Talking-head Generation with Rhythmic Head Motion" presents a novel approach for generating talking-head videos, which not only lip-sync with audio input but also exhibit natural head movements. Existing techniques typically focus on static face generation, or they rely on landmarks and video frames that often result in unrealistic or unstable synthesis. This paper introduces a sophisticated framework that combines a 3D-aware generative network, a hybrid embedding module, and a non-linear composition module to overcome these limitations.

Core Contributions

The research makes several key contributions to the field of talking-head generation:

  1. 3D-aware Generative Network: This component is instrumental in managing head motion and facial expressions independently, thereby avoiding the convoluted deformations present in previous methods. By employing explicit 3D modeling techniques, the network can generate head movements that are temporally coherent and visually plausible.
  2. Hybrid Embedding Module: This module dynamically aggregates appearance information from a set of reference images, effectively embedding individual characteristics into the generated frames. By approximating relationships between target and reference images, the module enhances the network's ability to preserve identity across different video frames.
  3. Non-linear Composition Module: This module addresses the challenge of synthesizing realistic backgrounds and facial features during significant head movements. By using non-linear composition techniques to integrate 3D-model information and image data, it significantly reduces visual discontinuities commonly associated with GAN-based approaches.

Experimental Analysis

The authors conducted extensive experiments on standard benchmarks, including VoxCeleb2 and LRS3-TED datasets. The results indicate that their method surpasses state-of-the-art approaches in both quantitative measures (e.g., SSIM, CSIM, FID) and qualitative assessments through user studies. The method achieves notable improvements in identity preservation, lip-sync accuracy, and temporal coherence of head movements.

Implications and Future Directions

Practically, this research offers promising implications for real-world applications like enhancing visual communication in assistive technologies for hearing-impaired users and creating lifelike virtual characters for media and gaming industries. Theoretically, the disentanglement of head motion and facial expressions could advance adversarial training strategies and improve supervised learning models by providing more nuanced datasets.

Looking forward, although this approach effectively manages typical head motions, challenges remain in synthesizing extreme poses or incorporating dynamic environmental factors such as variable lighting and camera movements. Future advancements might explore these aspects by integrating more complex environmental modeling or adaptive audio-visual correlations.

In summary, this paper provides a well-structured, innovative framework for talking-head video generation that addresses significant gaps in existing models. Its approach to motion disentanglement and identity preservation opens new avenues for enhancing human-computer interaction and digital media technologies.