  Text-driven human motion generation, as one of the vital tasks in
computer-aided content creation, has recently attracted increasing attention.
While pioneering research has largely focused on improving numerical
performance metrics on given datasets, practical applications reveal a common
challenge: existing methods often overfit specific motion expressions in the
training data, hindering their ability to generalize to novel descriptions like
unseen combinations of motions. This limitation restricts their broader
applicability. We argue that the aforementioned problem primarily arises from
the scarcity of available motion-text pairs, given the many-to-many nature of
text-driven motion generation. To tackle this problem, we formulate
text-to-motion generation as a Markov decision process and present
\textbf{InstructMotion}, which incorporate the trail and error paradigm in
reinforcement learning for generalizable human motion generation. Leveraging
contrastive pre-trained text and motion encoders, we delve into optimizing
reward design to enable InstructMotion to operate effectively on both paired
data, enhancing global semantic level text-motion alignment, and synthetic
text-only data, facilitating better generalization to novel prompts without the
need for ground-truth motion supervision. Extensive experiments on prevalent
benchmarks and also our synthesized unpaired dataset demonstrate that the
proposed InstructMotion achieves outstanding performance both quantitatively
and qualitatively.
