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GPAvatar: Generalizable and Precise Head Avatar from Image(s) (2401.10215v1)

Published 18 Jan 2024 in cs.CV

Abstract: Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community. The fundamental objective of this field is to faithfully recreate the head avatar and precisely control expressions and postures. Existing methods, categorized into 2D-based warping, mesh-based, and neural rendering approaches, present challenges in maintaining multi-view consistency, incorporating non-facial information, and generalizing to new identities. In this paper, we propose a framework named GPAvatar that reconstructs 3D head avatars from one or several images in a single forward pass. The key idea of this work is to introduce a dynamic point-based expression field driven by a point cloud to precisely and effectively capture expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion module in the tri-planes canonical field to leverage information from multiple input images. The proposed method achieves faithful identity reconstruction, precise expression control, and multi-view consistency, demonstrating promising results for free-viewpoint rendering and novel view synthesis.

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Authors (7)
  1. Xuangeng Chu (7 papers)
  2. Yu Li (378 papers)
  3. Ailing Zeng (58 papers)
  4. Tianyu Yang (67 papers)
  5. Lijian Lin (11 papers)
  6. Yunfei Liu (40 papers)
  7. Tatsuya Harada (142 papers)
Citations (10)
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Summary

  • The paper introduces a novel approach that reconstructs head avatars from static or dynamic images with remarkable precision.
  • It integrates advanced image reconstruction and reenactment techniques to maintain detailed and consistent facial features.
  • Extensive experiments validate its superiority in realism while outlining ethical guidelines to promote responsible avatar synthesis.

GPAvatar: Generalizable and Precise Head Avatar from Image(s)

The paper "GPAvatar: Generalizable and Precise Head Avatar from Image(s)" introduces a novel framework for reconstructing head avatars from one or multiple images. The proposed method offers a generalized solution that emphasizes precision in the generated avatars, addressing a key challenge in avatar reconstruction—achieving high fidelity in capturing intricate details of the subject's face.

Methodology

The GPAvatar framework leverages advanced modeling techniques to synthesize precise head avatars, employing a flexible architecture that can adapt across varying input conditions. This adaptability is particularly beneficial when dealing with a diverse range of facial features and expressions, as the model can retain the unique characteristics of different subjects with minimal degradation in performance.

The methodological advancement is underscored by the innovative integration of both image reconstruction and reenactment capabilities into the framework. This combined approach allows for the generation of animated head avatars from static images, which broadens the applicability to include realistic video synthesis based on limited visual information.

Experimental Results

Through extensive experimentation, the authors have demonstrated substantial improvements in avatar realism and stability. Quantitative evaluations reveal that GPAvatar outperforms existing solutions in terms of detail preservation and generalization capabilities. The framework's ability to maintain consistent performance across varied datasets underscores its potential utility in real-world applications, such as virtual reality and interactive media.

Ethical Considerations

The paper presents a thorough discussion on the ethical implications associated with head avatar generation technology, particularly the risks of misuse in creating deepfakes. The authors propose several preventive measures:

  • Employing visible and invisible watermarks to identify synthesized videos and link them to their creators.
  • Restricting the synthesis of avatars to virtual identities unless explicit consent is obtained for real individuals.
  • Encouraging the use of the technology for legitimate purposes, such as education or authorized content creation.

These measures aim to mitigate ethical risks while promoting responsible use of the technology.

Implications and Future Directions

The practical implications of GPAvatar are significant. In interactive domains, precise avatar reconstruction can enhance user experience by enabling more realistic character interactions. The paper suggests potential advancements in virtual education, personalized media, and content creation, where high-quality avatars could serve as effective substitutes for real actors or presenters.

Theoretically, the methodology set forth in this paper could pave the way for further studies into avatar realism, focusing on aspects such as dynamic facial expressions and emotion conveyance. Subsequent research could investigate the integration of additional sensory inputs to enhance avatar interactivity and immersion.

The release of GPAvatar's codebase offers a valuable opportunity for the research community to build upon this work, enhancing reproducibility and fostering collaboration. In conclusion, GPAvatar stands as a promising contribution to the field of avatar synthesis, offering a rigorously tested, ethically mindful tool for future explorations in AI-powered face generation.

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GitHub

  1. GitHub - xg-chu/GPAvatar: [ICLR 2024] Generalizable and Precise Head Avatar from Image(s) (246 stars)