PoseMaster: Generating 3D Characters in Arbitrary Poses from a Single Image

• URL: http://arxiv.org/abs/2506.21076v1
• Date: Thu, 26 Jun 2025 08:03:14 GMT
• Title: PoseMaster: Generating 3D Characters in Arbitrary Poses from a Single Image
• Authors: Hongyu Yan, Kunming Luo, Weiyu Li, Yixun Liang, Shengming Li, Jingwei Huang, Chunchao Guo, Ping Tan,
• Abstract summary: We propose PoseMaster, an end-to-end controllable 3D character generation framework.<n>Specifically, we unify pose transformation and 3D character generation into a flow-based 3D native generation framework.<n>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.
• Score: 37.332231168919705
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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