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Ariana Bermudez Venegas

dblp:378/1524 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-2826-7666ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%
Artificial intelligence
1 paper
Face, body and person analysis · 100%
Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Face, body and person analysis › face manipulation
facial expression transfer
0.812024
VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence · ACM Trans. Graph. 2024
Immersive interaction
telepresence
0.812024
VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence · ACM Trans. Graph. 2024
Immersive interaction
virtual reality
0.812024
VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence · ACM Trans. Graph. 2024

Methods — techniques the papers use, named apart from their topics

transformer blocks · 2.3multi-stage self-supervision · 2.3cross-reenactment architecture · 2.3
YearPublicationVenuePosition
2024 VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence
abstract
We introduce VOODOO XP: a 3D-aware one-shot head reenactment method that can generate highly expressive facial expressions driven by an input video from a single 2D portrait. Our approach is real-time, view-consistent, and can be instantly used without calibration or fine-tuning. We demonstrate our solution in 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 a new multi-stage self-supervision approach. It relies on a coarse-to-fine training strategy, which is combined with 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. Furthermore, our proposed method demonstrates state-of-the-art expressiveness and likeness preservation on diverse subjects and capture conditions.
Egor Zakharov, Long-Nhat Ho, Adilbek Karmanov, Ariana Bermudez Venegas, McLean Goldwhite, Aviral Agarwal, Liwen Hu 0001, Anh Tuan Tran 0001, Hao Li 0015
ACM Trans. Graph.5