In this paper, we introduce LGTM, a novel Local-to-Global pipeline for
Text-to-Motion generation. LGTM utilizes a diffusion-based architecture and
aims to address the challenge of accurately translating textual descriptions
into semantically coherent human motion in computer animation. Specifically,
traditional methods often struggle with semantic discrepancies, particularly in
aligning specific motions to the correct body parts. To address this issue, we
propose a two-stage pipeline to overcome this challenge: it first employs large
language models (LLMs) to decompose global motion descriptions into
part-specific narratives, which are then processed by independent body-part
motion encoders to ensure precise local semantic alignment. Finally, an
attention-based full-body optimizer refines the motion generation results and
guarantees the overall coherence. Our experiments demonstrate that LGTM gains
significant improvements in generating locally accurate, semantically-aligned
human motion, marking a notable advancement in text-to-motion applications.
Code and data for this paper are available at this https URL

本研究介绍了 LGTM，一种面向文本到动作生成的新颖的本地到全局流程。LGTM 利用扩散式架构，旨在解决将文本描述准确转化为在计算机动画中语义一致的人体动作的挑战。我们通过引入两阶段的流程来克服语义差异的问题，首先使用大型语言模型将全局动作描述分解为特定部位的叙述，然后使用独立的身体部位运动编码器处理以确保准确的局部语义对齐。最后，基于注意力机制的全身优化器对运动生成结果进行细化，并确保整体一致性。实验结果表明，LGTM 在生成局部准确、语义对齐的人体动作方面取得了显著改进，标志着文本到动作应用的重要进展。