  Text-driven 3D stylization is a complex and crucial task in the fields of
computer vision (CV) and computer graphics (CG), aimed at transforming a bare
mesh to fit a target text. Prior methods adopt text-independent multilayer
perceptrons (MLPs) to predict the attributes of the target mesh with the
supervision of CLIP loss. However, such text-independent architecture lacks
textual guidance during predicting attributes, thus leading to unsatisfactory
stylization and slow convergence. To address these limitations, we present
X-Mesh, an innovative text-driven 3D stylization framework that incorporates a
novel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically
integrates the guidance of the target text by utilizing text-relevant spatial
and channel-wise attentions during vertex feature extraction, resulting in more
accurate attribute prediction and faster convergence speed. Furthermore,
existing works lack standard benchmarks and automated metrics for evaluation,
often relying on subjective and non-reproducible user studies to assess the
quality of stylized 3D assets. To overcome this limitation, we introduce a new
standard text-mesh benchmark, namely MIT-30, and two automated metrics, which
will enable future research to achieve fair and objective comparisons. Our
extensive qualitative and quantitative experiments demonstrate that X-Mesh
outperforms previous state-of-the-art methods.
