GNOCHI：用于近距离人类交互的生成神经模型
GNOCHI: Generative Neural mOdel for Close Human-Human Interactions
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中文总结 AI 辅助
该研究针对虚拟环境中3D人类交互难题，提出用条件变分自编码器的生成模型，解决数据稀缺与碰撞感知问题，经自动监督数据增强和自监督损失训练，能生成多样合理且物理正确的交互，超越现有方法。
中文摘要 AI 辅助
在虚拟环境中创建逼真的3D人类交互具有挑战性，因为人体自由度高且需要物理上准确且不相互碰撞的姿势。传统方法缺乏生成能力，近期生成方法在建模近距离交互方面存在不足。本文引入一种使用条件变分自编码器（cVAE）的新型生成模型，用于近距离3D人类交互，可根据另一人的姿势生成一人的姿势，实现可控且多样的交互合成。为训练模型，解决了数据稀缺和生成方法中的碰撞感知两个长期挑战，提出自动监督数据增强策略和基于碰撞解决技术的自监督损失。通过广泛评估，展示了当前方法无法生成的各种合理且物理正确的交互。
英文摘要
Creating realistic 3D human-human interactions in virtual environments is challenging due to the high degrees of freedom in the human body and the need for physically accurate poses that do not collide with each other. Traditional methods for human-human interaction are based on motion tracking or 3D body reconstruction, but lack generative capabilities. Recent generative methods enable the synthesis of individual or interacting motions via text or image input, but generally fall short in modeling close interactions. This paper introduces a novel generative model for close 3D human-human interactions using a conditional variational autoencoder (cVAE), which generates poses for one human conditioned on the pose of another, allowing for controlled and diverse interaction synthesis. To train our model, we address two underlying long-standing challenges in the field of human-human interaction: data scarcity, for which we propose an automated supervised data augmentation strategy that generates synthetic yet realistic interaction poses; and collision awareness in generative approaches, for which we propose a self-supervised loss based on a collision resolution technique using volumetric proxies to ensure physically correct interactions. We extensively evaluate the capabilities of our model, and demonstrate a wide variety of plausible and physically correct interactions, not possible to generate with current state-of-the-art methods.
发表机构
- Universidad Rey Juan Carlos（卡洛斯三世大学）
机构由 AI 辅助整理，请以论文原文为准。
