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Researchers use Transformers to generate reactive human motion from interaction data

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed Transformer-based models to generate human motion in interactive scenarios, focusing on how one person's movement influences another's. They created a dataset from boxing videos to train and compare models like the simple Transformer, iTransformer, and Crossformer. The study found that a basic Transformer model, enhanced with person ID embeddings, effectively generated plausible, interaction-aware motions without posture collapse, outperforming more complex architectures that accumulated errors. AI

影响 Introduces a method for generating realistic, interaction-aware human motion, potentially impacting animation and virtual reality.

排序理由 Academic paper on a novel approach to human motion generation using Transformer models.

在 arXiv cs.CV 阅读 →

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Researchers use Transformers to generate reactive human motion from interaction data

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Masato Soga, Ryuki Takebayashi ·

    Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models

    arXiv:2604.22164v1 Announce Type: new Abstract: Recent advances in deep learning have enabled the generation of videos from textual descriptions as well as the prediction of future sequences from input videos. Similarly, in human motion modeling, motions can be generated from tex…

  2. arXiv cs.CV TIER_1 English(EN) · Ryuki Takebayashi ·

    Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models

    Recent advances in deep learning have enabled the generation of videos from textual descriptions as well as the prediction of future sequences from input videos. Similarly, in human motion modeling, motions can be generated from text or predicted from a single person's motion seq…

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