The generation of humanoid animation from text prompts can profoundly impact animation production and AR/VR experiences. However, existing methods only generate body motion data, excluding facial expressions and hand movements. This limitation, primarily due to a lack of a comprehensive whole-body motion dataset, inhibits their readiness for production use. Recent attempts to create such a dataset have resulted in either motion inconsistency among different body parts in the artificially augmented data or lower quality in the data extracted from RGB videos. In this work, we propose T2M-X, a two-stage method that learns expressive text-to-motion generation from partially annotated data. T2M-X trains three separate Vector Quantized Variational AutoEncoders (VQ-VAEs) for body, hand, and face on respective high-quality data sources to ensure high-quality motion outputs, and a Multi-indexing Generative Pretrained Transformer (GPT) model with motion consistency loss for motion generation and coordination among different body parts. Our results show significant improvements over the baselines both quantitatively and qualitatively, demonstrating its robustness against the dataset limitations.

本研究针对文本提示生成类人动画中的面部表情和手部动作缺失问题，提出了一种两阶段的方法T2M-X，从部分标注的数据中学习表现性文本到动作生成。通过训练三种独立的矢量量化变分自编码器和多索引生成预训练变换器，该方法显著提高了生成运动的一致性和质量，显示出在数据集局限下的稳健性。

T2M-X：从部分标注数据中学习表现性文本到动作生成