Make-It-Poseable: Feed-forward Latent Posing Model for 3D Humanoid Character Animation
Posing 3D characters is a fundamental task in computer graphics and vision. However, existing methods like auto-rigging and pose-conditioned generation often struggle with challenges such as inaccurate skinning weight prediction, topological imperfections, and poor pose conformance, limiting their robustness and generalizability. To overcome these limitations, we introduce Make-It-Poseable, a novel feed-forward framework that reformulates character posing as a latent-space transformation problem. Instead of deforming mesh vertices as in traditional pipelines, our method reconstructs the character in new poses by directly manipulating its latent representation. At the core of our method is a latent posing transformer that manipulates shape tokens based on skeletal motion. This process is facilitated by a dense pose representation for precise control. To ensure high-fidelity geometry and accommodate topological changes, we also introduce a latent-space supervision strategy and an adaptive completion module. Our method demonstrates superior performance in posing quality. It also naturally extends to 3D editing applications like part replacement and refinement.
6944eaa1fbf17e708e18612e
Zhiyang Guo
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2025-12-19T08:57:24.640Z
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Zhiyang Guo
jasongzy
user
jasongzy
6944eaa1fbf17e708e18612f
Ori Zhang
6944eaa1fbf17e708e186130
Jax Xiang
6944eaa1fbf17e708e186131
Alan Zhao
6944eaa1fbf17e708e186132
Wengang Zhou
6944eaa1fbf17e708e186133
Houqiang Li
Make-It-Poseable: Feed-forward Latent Posing Model for 3D Humanoid Character Animation
Posing 3D characters is a fundamental task in computer graphics and vision. However, existing methods like auto-rigging and pose-conditioned generation often struggle with challenges such as inaccurate skinning weight prediction, topological imperfections, and poor pose conformance, limiting their robustness and generalizability. To overcome these limitations, we introduce Make-It-Poseable, a novel feed-forward framework that reformulates character posing as a latent-space transformation problem. Instead of deforming mesh vertices as in traditional pipelines, our method reconstructs the character in new poses by directly manipulating its latent representation. At the core of our method is a latent posing transformer that manipulates shape tokens based on skeletal motion. This process is facilitated by a dense pose representation for precise control. To ensure high-fidelity geometry and accommodate topological changes, we also introduce a latent-space supervision strategy and an adaptive completion module. Our method demonstrates superior performance in posing quality. It also naturally extends to 3D editing applications like part replacement and refinement.
A novel feed-forward framework, Make-It-Poseable, reformulates character posing as a latent-space transformation problem, using a latent posing transformer and dense pose representation to achieve superior posing quality and extend to 3D editing applications.
feed-forward framework latent-space transformation latent posing transformer shape tokens dense pose representation latent-space supervision adaptive completion module