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MARRS framework generates coordinated human reaction motions using continuous representations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MARRS, a novel framework for synthesizing human reactions conditioned on observed actions. The system utilizes a Unit-distinguished Motion Variational AutoEncoder (UD-VAE) to encode distinct body and hand units independently. It incorporates Action-Conditioned Fusion (ACF) to process reactive tokens and Mutual Unit Modulation (MUM) to enable interaction between body and hand units. A compact MLP serves as a noise predictor within a diffusion model for generating token probability distributions. AI

IMPACT Introduces a new method for generating coordinated human reaction motions, potentially improving embodied AI and animation.

RANK_REASON This is a research paper detailing a novel framework for human action-reaction synthesis.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MARRS framework generates coordinated human reaction motions using continuous representations

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This is a research paper detailing a novel framework for human action-reaction synthesis.
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yabiao Wang, Shuo Wang, Jiangning Zhang, Jiafu Wu, Qingdong He, Yong Liu ·

    MARRS: Masked Autoregressive Unit-based Reaction Synthesis

    arXiv:2505.11334v4 Announce Type: replace Abstract: This work aims at a challenging task: human action-reaction synthesis, i.e., generating human reactions conditioned on the action sequence of another person. Currently, autoregressive modeling approaches with vector quantization…

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