标题： PoseLLM：通过MLP对齐增强语言引导的人体姿态估计 显示英文标题

标题： PoseLLM: Enhancing Language-Guided Human Pose Estimation with MLP Alignment

摘要： 人体姿态估计传统上依赖于编码关键点先验的架构，这限制了它们在新姿态或未见过的关键点上的泛化能力。 最近的语言引导方法如LocLLM将关键点定位重新表述为一个视觉-语言任务，通过文本描述实现零样本泛化。 然而，LocLLM的线性投影器无法捕捉对高精度定位至关重要的复杂空间-文本交互。 为了解决这个问题，我们提出了PoseLLM，第一个基于大型语言模型（LLM）的姿态估计框架，该框架用非线性MLP视觉-语言连接器替换了线性投影器。 这个带有GELU激活函数的轻量级两层MLP实现了分层跨模态特征转换，增强了视觉块和文本关键点描述的融合。 仅在COCO数据上训练，PoseLLM在COCO验证集上达到了77.8 AP，比LocLLM高出+0.4 AP，同时在Human-Art和MPII上保持了强大的零样本泛化能力。 我们的工作表明，一个简单而强大的非线性连接器显著提高了定位精度，而不会牺牲泛化能力，推动了语言引导姿态估计的最先进技术。 代码可在https://github.com/Ody-trek/PoseLLM获取。 显示英文摘要

摘要： Human pose estimation traditionally relies on architectures that encode keypoint priors, limiting their generalization to novel poses or unseen keypoints. Recent language-guided approaches like LocLLM reformulate keypoint localization as a vision-language task, enabling zero-shot generalization through textual descriptions. However, LocLLM's linear projector fails to capture complex spatial-textual interactions critical for high-precision localization. To address this, we propose PoseLLM, the first Large Language Model (LLM)-based pose estimation framework that replaces the linear projector with a nonlinear MLP vision-language connector. This lightweight two-layer MLP with GELU activation enables hierarchical cross-modal feature transformation, enhancing the fusion of visual patches and textual keypoint descriptions. Trained exclusively on COCO data, PoseLLM achieves 77.8 AP on the COCO validation set, outperforming LocLLM by +0.4 AP, while maintaining strong zero-shot generalization on Human-Art and MPII. Our work demonstrates that a simple yet powerful nonlinear connector significantly boosts localization accuracy without sacrificing generalization, advancing the state-of-the-art in language-guided pose estimation. Code is available at https://github.com/Ody-trek/PoseLLM.