A 3D Gaussian avatar model that conditions non-rigid deformation on hierarchical skeleton and vertex motion reaches state-of-the-art rendering quality on three human-capture datasets.
Motion-Aware Animatable Gaussian Avatars Deblurring
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The creation of 3D human avatars from multi-view videos is a significant yet challenging task in computer vision. However, existing techniques rely on high-quality, sharp images as input, which are often impractical to obtain in real-world scenarios due to variations in human motion speed and intensity. This paper introduces a novel method for directly reconstructing sharp 3D human Gaussian avatars from blurry videos. The proposed approach incorporates a 3D-aware, physics-based model of blur formation caused by human motion, together with a 3D human motion model designed to resolve ambiguities in motion-induced blur. This framework enables the joint optimization of the avatar representation and motion parameters from a coarse initialization. Comprehensive benchmarks are established using both a synthetic dataset and a real-world dataset captured with a 360-degree synchronous hybrid-exposure camera system. Extensive evaluations demonstrate the effectiveness of the model across diverse conditions. Codes Available: https://github.com/MyNiuuu/MAD-Avatar
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Sequential Gaussian Avatars with Hierarchical Motion Context
A 3D Gaussian avatar model that conditions non-rigid deformation on hierarchical skeleton and vertex motion reaches state-of-the-art rendering quality on three human-capture datasets.