X-MoGen: Unified Motion Generation across Humans and Animals

TL;DR

X-MoGen introduces a unified framework for cross-species text-driven motion generation, enabling realistic motion synthesis for both humans and animals by addressing morphological differences with a novel two-stage architecture and a large-scale dataset.

Contribution

It is the first unified approach to generate cross-species motion from text, combining a novel architecture with a comprehensive dataset for joint training.

Findings

Outperforms existing methods on seen and unseen species

Achieves high skeletal plausibility across diverse species

Demonstrates effective generalization in motion generation

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 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 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.