Papers › Role-Aware Interaction Generation from Textual Description

Role-Aware Interaction Generation from Textual Description

1 Jan 2023ICCV 2023 1archive 2025-07-28

Mikihiro Tanaka, Kent Fujiwara

This research tackles the problem of generating interaction between two human actors corresponding to textual description. We claim that certain interactions, which we call asymmetric interactions, involve a relationship between an actor and a receiver, whose motions significantly differ depending on the assigned role. However, existing studies of interaction generation attempt to learn the correspondence between a single label and the motions of both actors combined, overlooking differences in individual roles. We consider a novel problem of role-aware interaction generation, where roles can be designated before generation. We translate the text of the asymmetric interactions into active and passive voice to ensure the textual context is consistent with each role. We propose a model that learns to generate motions of the designated role, which together form a mutually consistent interaction. As the model treats individual motions separately, it can be pretrained to derive knowledge from single-person motion data for more accurate interactions. Moreover, we introduce a method inspired by Permutation Invariant Training (PIT) that can automatically learn which of the two actions corresponds to an actor or a receiver without additional annotation. We further present cases where existing evaluation metrics fail to accurately assess the quality of generated interactions, and propose a novel metric, Mutual Consistency, to address such shortcomings. Experimental results demonstrate the efficacy of our method, as well as the necessity of the proposed metric. Our code is available at https://github.com/line/Human-Interaction-Generation.

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Code

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Motion Synthesis

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Motion Synthesis InterHuman RIG FID 6.775 #6 of 10 Archive leaderboard report
Motion Synthesis InterHuman RIG MMDist 5.876 #6 of 10 Archive leaderboard report
Motion Synthesis InterHuman RIG MModality 2.141 #6 of 10 Archive leaderboard report
Motion Synthesis InterHuman RIG R-Precision Top3 0.521 #6 of 10 Archive leaderboard report

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Methods

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