ARMFlow: AutoRegressive MeanFlow for Online 3D Human Reaction Generation

• URL: http://arxiv.org/abs/2512.16234v1
• Date: Thu, 18 Dec 2025 06:28:42 GMT
• Title: ARMFlow: AutoRegressive MeanFlow for Online 3D Human Reaction Generation
• Authors: Zichen Geng, Zeeshan Hayder, Wei Liu, Hesheng Wang, Ajmal Mian,
• Abstract summary: 3D human reaction generation faces three main challenges: high motion fidelity, real-time inference, and autoregressive adaptability for online scenarios.<n>We propose ARMFlow, a MeanFlow-based autoregressive framework that models temporal dependencies between motions and velocity.<n>Our single-step online generation surpasses existing methods on InterHuman and InterX by over 40% in FID, while matching offline state-of-the-art performance despite using only partial sequence conditions.
• Score: 48.716675019745885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D human reaction generation faces three main challenges:(1) high motion fidelity, (2) real-time inference, and (3) autoregressive adaptability for online scenarios. Existing methods fail to meet all three simultaneously. We propose ARMFlow, a MeanFlow-based autoregressive framework that models temporal dependencies between actor and reactor motions. It consists of a causal context encoder and an MLP-based velocity predictor. We introduce Bootstrap Contextual Encoding (BSCE) in training, encoding generated history instead of the ground-truth ones, to alleviate error accumulation in autoregressive generation. We further introduce the offline variant ReMFlow, achieving state-of-the-art performance with the fastest inference among offline methods. Our ARMFlow addresses key limitations of online settings by: (1) enhancing semantic alignment via a global contextual encoder; (2) achieving high accuracy and low latency in a single-step inference; and (3) reducing accumulated errors through BSCE. Our single-step online generation surpasses existing online methods on InterHuman and InterX by over 40% in FID, while matching offline state-of-the-art performance despite using only partial sequence conditions.

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