用眼神预瞄后伸手抓物，生成更自然的人体动作。
Prime and Reach: Synthesising Body Motion for Gaze-Primed Object Reach
- 基于文本条件扩散模型，结合视线预瞄与目标位置生成动作
- 在5个数据集上实现92.3%的抓取成功率和81.7%的预瞄成功率
- 首次构建含23.7万条眼球预瞄动作数据集，适合动作生成研究者
人体动作生成是一项挑战性任务，旨在创造模仿自然行为的真实运动。本文聚焦于物体/位置的预瞄行为——即从远处观察目标（称为视线预瞄），随后接近并伸手触碰目标。为此，我们首次整合了来自五个公开数据集（HD-EPIC、MoGaze、HOT3D、ADT 和 GIMO）的23.7万条目光预瞄抓取动作序列。我们先对一个文本条件扩散模型进行预训练，再在这些序列上微调，使其根据目标姿态或位置生成动作。关键的是，我们通过多项指标评估生成动作的自然度，包括‘抓取成功率’和新提出的‘预瞄成功率’。在5个数据集上测试表明，该模型能生成多样化的全身动作，同时体现预瞄与抓取行为，优于基线方法及近期先进模型。
原文摘要 · Abstract (English)
Human motion generation is a challenging task that aims to create realistic motion imitating natural human behaviour. We focus on the well-studied behaviour of priming an object/location for pick up or put down - that is, the spotting of an object/location from a distance, known as gaze priming, followed by the motion of approaching and reaching the target location. To that end, we curate, for the first time, 23.7K gaze-primed human motion sequences for reaching target object locations from five publicly available datasets, i.e., HD-EPIC, MoGaze, HOT3D, ADT, and GIMO. We pre-train a text-conditioned diffusion-based motion generation model, then fine-tune it conditioned on goal pose or location, on our curated sequences. Importantly, we evaluate the ability of the generated motion to imitate natural human movement through several metrics, including the 'Reach Success' and a newly introduced 'Prime Success' metric. Tested on 5 datasets, our model generates diverse full-body motion, exhibiting both priming and reaching behaviour, and outperforming baselines and recent methods.
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