MIME: Multimodal Interactive Motion Encoder
This paper introduces the Multimodal Interactive Motion Encoder (MIME), the first dedicated multimodal encoder for two-person interactive motion. MIME uses stream-based co-attention with explicit interaction features and curriculum-based contrastive training to capture individual and shared structures. On Inter-X text-motion retrieval, MIME achieves a 12.8% relative improvement in text-to-motion R@1 over baselines. As a frozen auxiliary prior, MIME improves semantic alignment in TIMotion and InterMask while maintaining FID, demonstrating that interaction-aware multimodal encoding enhances multi-person motion retrieval and transfers to downstream motion generation.
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[Submitted on 18 Jul 2026]
Title:MIME: Multimodal Interactive Motion Encoder
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Abstract:Text-motion representation learning has advanced rapidly, with growing interest in multi person interactions for animation, AR/VR, and embodied AI. These settings require representations that align language with both individual actor dynamics and the relationships between actors. We introduce the Multimodal Interactive Motion Encoder (MIME), which, to our knowledge, represents the first dedicated multimodal encoder designed specifically for two person interactive motion. MIME captures individual and shared structure using stream based co-attention with explicit interaction features and curriculum based contrastive training. On Inter-X text-motion retrieval, MIME consistently outperforms early and late fusion baselines across gallery sizes, achieving a 12.8% relative improvement in text-to-motion R@1 at a 2,000-sample gallery. We further evaluate MIME as a frozen auxiliary prior within TIMotion and InterMask on the unseen InterHuman dataset. MIME improves semantic alignment metrics while maintaining comparable FID in TIMotion. These results show that interaction aware multimodal encoding improves multi person motion retrieval and transfers across datasets to support downstream motion generation.
Comments: Under review at WACV 2027
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.22702 [cs.CV]
(or arXiv:2607.22702v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22702
arXiv-issued DOI via DataCite (pending registration)
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From: Addison Zucek [view email] [v1] Sat, 18 Jul 2026 20:08:31 UTC (6,946 KB)
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