Current techniques face difficulties in generating motions from intricate
semantic descriptions, primarily due to insufficient semantic annotations in
datasets and weak contextual understanding. To address these issues, we present
SemanticBoost, a novel framework that tackles both challenges simultaneously.
Our framework comprises a Semantic Enhancement module and a Context-Attuned
Motion Denoiser (CAMD). The Semantic Enhancement module extracts supplementary
semantics from motion data, enriching the dataset's textual description and
ensuring precise alignment between text and motion data without depending on
large language models. On the other hand, the CAMD approach provides an
all-encompassing solution for generating high-quality, semantically consistent
motion sequences by effectively capturing context information and aligning the
generated motion with the given textual descriptions. Distinct from existing
methods, our approach can synthesize accurate orientational movements, combined
motions based on specific body part descriptions, and motions generated from
complex, extended sentences. Our experimental results demonstrate that
SemanticBoost, as a diffusion-based method, outperforms auto-regressive-based
techniques, achieving cutting-edge performance on the Humanml3D dataset while
maintaining realistic and smooth motion generation quality.

SemanticBoost 是通过 Semantic Enhancement 模块和 Context-Attuned Motion Denoiser 方法同时解决数据集中语义注释不足和语境理解不强的问题，以生成高质量、语义一致的运动序列的一种新颖框架。