OFERA: Blendshape-driven 3D Gaussian Control for Occluded Facial Expression to Realistic Avatars in VR
Seokhwan Yang, Boram Yoon, Seoyoung Kang, Hail Song, Woontack Woo

TL;DR
OFERA introduces a real-time, blendshape-driven framework for controlling photorealistic Gaussian head avatars in VR, overcoming privacy and discomfort issues of sensor or camera-based methods.
Contribution
It presents a novel end-to-end pipeline leveraging blendshape signals for real-time, realistic avatar expression control in VR without additional sensors or cameras.
Findings
EPM outperforms existing mapping methods on quantitative metrics.
User study shows OFERA enhances expression fidelity and avatar realism.
Framework enables real-time, photorealistic avatar expression control in VR.
Abstract
We propose OFERA, a novel framework for real-time expression control of photorealistic Gaussian head avatars for VR headset users. Existing approaches attempt to recover occluded facial expressions using additional sensors or internal cameras, but sensor-based methods increase device weight and discomfort, while camera-based methods raise privacy concerns and suffer from limited access to raw data. To overcome these limitations, we leverage the blendshape signals provided by commercial VR headsets as expression inputs. Our framework consists of three key components: (1) Blendshape Distribution Alignment (BDA), which applies linear regression to align the headset-provided blendshape distribution to a canonical input space; (2) an Expression Parameter Mapper (EPM) that maps the aligned blendshape signals into an expression parameter space for controlling Gaussian head avatars; and (3) a…
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Taxonomy
TopicsFace recognition and analysis · Emotion and Mood Recognition · Virtual Reality Applications and Impacts
