Kavli Affiliate: Cheng Peng
| First 5 Authors: Cheng Peng, Jingxiang Sun, Yushuo Chen, Zhaoqi Su, Zhuo Su
| Summary:
Photorealistic and animatable human avatars are a key enabler for
virtual/augmented reality, telepresence, and digital entertainment. While
recent advances in 3D Gaussian Splatting (3DGS) have greatly improved rendering
quality and efficiency, existing methods still face fundamental challenges,
including time-consuming per-subject optimization and poor generalization under
sparse monocular inputs. In this work, we present the Parametric Gaussian Human
Model (PGHM), a generalizable and efficient framework that integrates human
priors into 3DGS for fast and high-fidelity avatar reconstruction from
monocular videos. PGHM introduces two core components: (1) a UV-aligned latent
identity map that compactly encodes subject-specific geometry and appearance
into a learnable feature tensor; and (2) a disentangled Multi-Head U-Net that
predicts Gaussian attributes by decomposing static, pose-dependent, and
view-dependent components via conditioned decoders. This design enables robust
rendering quality under challenging poses and viewpoints, while allowing
efficient subject adaptation without requiring multi-view capture or long
optimization time. Experiments show that PGHM is significantly more efficient
than optimization-from-scratch methods, requiring only approximately 20 minutes
per subject to produce avatars with comparable visual quality, thereby
demonstrating its practical applicability for real-world monocular avatar
creation.
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