X-MoGen: Unified Motion Generation across Humans and Animals

• URL: http://arxiv.org/abs/2508.05162v1
• Date: Thu, 07 Aug 2025 08:51:51 GMT
• Title: X-MoGen: Unified Motion Generation across Humans and Animals
• Authors: Xuan Wang, Kai Ruan, Liyang Qian, Zhizhi Guo, Chang Su, Gaoang Wang,
• Abstract summary: X-MoGen is the first unified framework for cross-species text-driven motion generation covering both humans and animals.<n>We construct textbfUniMo4D, a large-scale dataset of 115 species and 119k motion sequences, which integrates human and animal motions under a shared skeletal topology for joint training.<n>Experiments on UniMo4D demonstrate that X-MoGen outperforms state-of-the-art methods on both seen and unseen species.
• Score: 9.967329240441844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-driven motion generation has attracted increasing attention due to its broad applications in virtual reality, animation, and robotics. While existing methods typically model human and animal motion separately, a joint cross-species approach offers key advantages, such as a unified representation and improved generalization. However, morphological differences across species remain a key challenge, often compromising motion plausibility. To address this, we propose \textbf{X-MoGen}, the first unified framework for cross-species text-driven motion generation covering both humans and animals. X-MoGen adopts a two-stage architecture. First, a conditional graph variational autoencoder learns canonical T-pose priors, while an autoencoder encodes motion into a shared latent space regularized by morphological loss. In the second stage, we perform masked motion modeling to generate motion embeddings conditioned on textual descriptions. During training, a morphological consistency module is employed to promote skeletal plausibility across species. To support unified modeling, we construct \textbf{UniMo4D}, a large-scale dataset of 115 species and 119k motion sequences, which integrates human and animal motions under a shared skeletal topology for joint training. Extensive experiments on UniMo4D demonstrate that X-MoGen outperforms state-of-the-art methods on both seen and unseen species.

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