mlsanityData to 14 Oct 2026

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

arXiv 2405.16204 ↗First posted 25 May 2024cs.CV cs.AI cs.GRcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 89 of its 132 references that are in the map. First posted on arXiv 25 May 2024.

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89 of 132 references.

References · 89

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50 most cited of 89 All 89 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 12; 2026: 6.

19 papers.

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  1. 2025Avat3r: Large Animatable Gaussian Reconstruction Model for High-fidelity 3D Head Avatarscited by 53
  2. 2024Joker: Conditional 3D Head Synthesis with Extreme Facial Expressionscited by 18
  3. 2025MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatarscited by 17
  4. 2025DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesiscited by 12
  5. 2025FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervisioncited by 10
  6. 2025Audio Driven Real-Time Facial Animation for Social Telepresencecited by 5
  7. 2025Controlling Avatar Diffusion with Learnable Gaussian Embeddingcited by 3
  8. 2025Bringing Your Portrait to 3D Presencecited by 3
  9. 2025AGORA: Adversarial Generation Of Real-time Animatable 3D Gaussian Head Avatarscited by 3
  10. 2025RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Datacited by 2
  11. 2025FlexAvatar: Flexible Large Reconstruction Model for Animatable Gaussian Head Avatars with Detailed Deformationcited by 2
  12. 2026VRGaussianAvatar: Integrating 3D Gaussian Avatars into VRcited by 1
7 more
  1. 2026Feed-forward Gaussian Registration for Head Avatar Creation and Editingcited by 1
  2. 2026FiCA: Feed-forward instant Gaussian Codec Avatars from a Single Portrait Imagecited by 1
  3. 2025Improving Facial Rig Semantics for Tracking and Retargetingcited by 0
  4. 2025Instant Expressive Gaussian Head Avatars at Over 100 FPScited by 0
  5. 2026GeoDiff4D: Geometry-Aware Diffusion for 4D Head Avatar Reconstructioncited by 0
  6. 2026AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Texturescited by 0
  7. 2026GHARP: Real-time Gaussian Head Animation from Large-scale Reconstruction Priorcited by 0