  Is the Text to Motion model robust? Recent advancements in Text to Motion
models primarily stem from more accurate predictions of specific actions.
However, the text modality typically relies solely on pre-trained Contrastive
Language-Image Pretraining (CLIP) models. Our research has uncovered a
significant issue with the text-to-motion model: its predictions often exhibit
inconsistent outputs, resulting in vastly different or even incorrect poses
when presented with semantically similar or identical text inputs. In this
paper, we undertake an analysis to elucidate the underlying causes of this
instability, establishing a clear link between the unpredictability of model
outputs and the erratic attention patterns of the text encoder module.
Consequently, we introduce a formal framework aimed at addressing this issue,
which we term the Stable Text-to-Motion Framework (SATO). SATO consists of
three modules, each dedicated to stable attention, stable prediction, and
maintaining a balance between accuracy and robustness trade-off. We present a
methodology for constructing an SATO that satisfies the stability of attention
and prediction. To verify the stability of the model, we introduced a new
textual synonym perturbation dataset based on HumanML3D and KIT-ML. Results
show that SATO is significantly more stable against synonyms and other slight
perturbations while keeping its high accuracy performance.
