PoseMaster: Generating 3D Characters in Arbitrary Poses from a Single Image
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
3D characters play a crucial role in our daily entertainment. To improve the
efficiency of 3D character modeling, recent image-based methods use two
separate models to achieve pose standardization and 3D reconstruction of the
A-pose character. However, these methods are prone to generating distorted and
degraded images in the pose standardization stage due to self-occlusion and
viewpoints, which further affects the geometric quality of the subsequent
reconstruction process. To tackle these problems, we propose PoseMaster, an
end-to-end controllable 3D character generation framework. Specifically, we
unify pose transformation and 3D character generation into a flow-based 3D
native generation framework. To achieve accurate arbitrary-pose control, we
propose to leverage the 3D body bones existing in the skeleton of an animatable
character as the pose condition. Furthermore, considering the specificity of
multi-condition control, we randomly empty the pose condition and the image
condition during training to improve the effectiveness and generalizability of
pose control. Finally, we create a high-quality pose-control dataset derived
from realistic character animation data to make the model learning the implicit
relationships between skeleton and skinning weights. Extensive experiments show
that PoseMaster outperforms current state-of-the-art techniques in both
qualitative and quantitative evaluations for A-pose character generation while
demonstrating its powerful ability to achieve precise control for arbitrary
poses.
