AffordGrasp：跨模态扩散用于感知意识抓取合成
AffordGrasp: Cross-Modal Diffusion for Affordance-Aware Grasp Synthesis
AI总结：
本文提出AffordGrasp，通过跨模态扩散模型生成准确反映物体几何和用户指令的抓取姿态，提升AR/VR和具身AI中的手-物交互质量。
摘要：
生成准确反映物体几何和用户指定交互语义的人类抓取姿态对于AR/VR和具身AI中的自然手-物交互至关重要。然而，现有语义抓取方法在3D物体表示与文本指令之间存在较大的模态差距，且常缺乏显式的空间或语义约束，导致物理无效或语义不一致的抓取。本文提出AffordGrasp，一种基于扩散的框架，能够生成物理稳定且语义忠实的高精度人类抓取姿态。我们首先引入了一种可扩展的标注流程，自动丰富手-物交互数据集，添加细粒度的结构化语言标签以捕捉交互意图。基于这些标注，AffordGrasp整合了具有感知意识的隐式手姿态表示与双条件扩散过程，使模型能够联合推理物体几何、空间感知和指令语义。一个分布调整模块进一步强制物理接触一致性和语义对齐。我们在四个基于HO-3D、OakInk、GRAB和AffordPose的指令增强基准上评估AffordGrasp，并观察到在抓取质量、语义准确性和多样性方面显著优于现有最先进方法。
英文摘要：
Generating human grasping poses that accurately reflect both object geometry and user-specified interaction semantics is essential for natural hand-object interactions in AR/VR and embodied AI. However, existing semantic grasping approaches struggle with the large modality gap between 3D object representations and textual instructions, and often lack explicit spatial or semantic constraints, leading to physically invalid or semantically inconsistent grasps. In this work, we present AffordGrasp, a diffusion-based framework that produces physically stable and semantically faithful human grasps with high precision. We first introduce a scalable annotation pipeline that automatically enriches hand-object interaction datasets with fine-grained structured language labels capturing interaction intent. Building upon these annotations, AffordGrasp integrates an affordance-aware latent representation of hand poses with a dual-conditioning diffusion process, enabling the model to jointly reason over object geometry, spatial affordances, and instruction semantics. A distribution adjustment module further enforces physical contact consistency and semantic alignment. We evaluate AffordGrasp across four instruction-augmented benchmarks derived from HO-3D, OakInk, GRAB, and AffordPose, and observe substantial improvements over state-of-the-art methods in grasp quality, semantic accuracy, and diversity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
