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VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence (2405.16204v2)

Published 25 May 2024 in cs.CV, cs.AI, and cs.GR

Abstract: We introduce VOODOO XP: a 3D-aware one-shot head reenactment method that can generate highly expressive facial expressions from any input driver video and a single 2D portrait. Our solution is real-time, view-consistent, and can be instantly used without calibration or fine-tuning. We demonstrate our solution on a monocular video setting and an end-to-end VR telepresence system for two-way communication. Compared to 2D head reenactment methods, 3D-aware approaches aim to preserve the identity of the subject and ensure view-consistent facial geometry for novel camera poses, which makes them suitable for immersive applications. While various facial disentanglement techniques have been introduced, cutting-edge 3D-aware neural reenactment techniques still lack expressiveness and fail to reproduce complex and fine-scale facial expressions. We present a novel cross-reenactment architecture that directly transfers the driver's facial expressions to transformer blocks of the input source's 3D lifting module. We show that highly effective disentanglement is possible using an innovative multi-stage self-supervision approach, which is based on a coarse-to-fine strategy, combined with an explicit face neutralization and 3D lifted frontalization during its initial training stage. We further integrate our novel head reenactment solution into an accessible high-fidelity VR telepresence system, where any person can instantly build a personalized neural head avatar from any photo and bring it to life using the headset. We demonstrate state-of-the-art performance in terms of expressiveness and likeness preservation on a large set of diverse subjects and capture conditions.

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Authors (10)
  1. Phong Tran (10 papers)
  2. Egor Zakharov (15 papers)
  3. Long-Nhat Ho (3 papers)
  4. Liwen Hu (18 papers)
  5. Adilbek Karmanov (4 papers)
  6. Aviral Agarwal (1 paper)
  7. Ariana Bermudez Venegas (1 paper)
  8. Anh Tuan Tran (17 papers)
  9. Hao Li (803 papers)
  10. Mclean Goldwhite (2 papers)
Citations (1)
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Summary

Insights on VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence

The paper introduces VOODOO XP, a novel method for 3D-aware one-shot head reenactment aimed at enhancing virtual reality (VR) telepresence. This approach allows for the creation of highly expressive facial renderings using just a single 2D portrait and a driver video. The method promises real-time performance with consistent facial geometry across multiple views, highlighting significant advancements over prior 2D head reenactment methods that struggle with preservation of identity and consistency from novel perspectives.

Technical Overview and Methodological Contributions

VOODOO XP addresses several challenges in the domain of neural head reenactment. The solution pivots around a cross-reenactment architecture, leveraging an innovative approach where the driver's facial expressions are directly transferred to the source image's 3D lifting module via transformer blocks. This strategy enables effective disentanglement of facial identity and expressions, a notable improvement over existing techniques.

The authors employ a multi-stage self-supervision method rooted in a coarse-to-fine strategy, which intricately combines face neutralization and 3D frontalization at the training outset. The method culminates in an end-to-end VR telepresence setup, utilizing Meta Quest Pro head-mounted displays (HMDs), accommodating highly dynamic expressions and a diverse range of head poses. This system architecture significantly benefits applications in immersive communication and remote collaboration, where facial expressiveness and interaction realism are critical.

Performance and Comparative Analysis

The paper documents VOODOO XP's state-of-the-art performance by adhering to robust evaluation metrics and methodologies. Through the employment of datasets that incorporate a wide array of facial expressions and capture conditions, the method demonstrates superior expressiveness and likeness preservation attributes, outperforming other contemporary solutions.

Relative to similar efforts, such as VOODOO 3D and 3D morphable models, the paper claims its method offers a finer granularity in expression synthesis while maintaining synchronization with the source identity, even in cases requiring complex facial dynamics, such as asymmetric expressions and fine-scale wrinkle rendering.

Implications and Future Trajectories

The implications of this research are manifold. Practically, the integration into VR systems can hugely augment user experience by rendering more lifelike and emotionally resonant avatars, thereby enhancing communication quality in virtual environments. Theoretically, this work expands on the efficacy of transformer networks in identity-expression disentanglement, potentially guiding future explorations around robust expression transfer mechanisms.

Moreover, it opens avenues for further research into optimizing neural reenactment techniques, addressing computational overhead, and enhancing photorealism. There is significant scope for integrating these methods with forthcoming advancements in neural rendering, such as NeRFs and Gaussian splatting, which could overcome current resolution constraints imposed by hardware requirements.

Conclusion

VOODOO XP stands out in its provision of a sophisticated and practical solution to one-shot head reenactment, advancing the frontiers of VR telepresence technology. Its multi-faceted approach—emphasizing expressive modeling, real-time adaptability, and view consistency—offers a promising trajectory for both the academic community and industry practitioners aiming to harness virtual environments' communicative potential. Future work, focusing on expanding its capabilities and integrating full-body representations, holds promise for even broader applications and adoption.

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