GNOCHI: Generative Neural mOdel for Close Human-Human Interactions

• URL: http://arxiv.org/abs/2607.10408v1
• Date: Sat, 11 Jul 2026 17:19:32 GMT
• Title: GNOCHI: Generative Neural mOdel for Close Human-Human Interactions
• Abstract summary: This paper introduces a novel generative model for close 3D human-human interactions using a conditional variational autoencoder (cVAE)<n>We address two underlying long-standing challenges in the field of human-human interaction: data scarcity and collision awareness.<n>We demonstrate a wide variety of plausible and physically correct interactions, not possible to generate with current state-of-the-art methods.
• Score: 7.759879304243429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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