EigenActor: Variant Body-Object Interaction Generation Evolved from Invariant Action Basis Reasoning

• URL: http://arxiv.org/abs/2503.00382v2
• Date: Tue, 04 Mar 2025 02:17:57 GMT
• Title: EigenActor: Variant Body-Object Interaction Generation Evolved from Invariant Action Basis Reasoning
• Authors: Xuehao Gao, Yang Yang, Shaoyi Du, Yang Wu, Yebin Liu, Guo-Jun Qi,
• Abstract summary: This paper explores a cross-modality synthesis task that infers 3D human-object interactions (HOIs) from a given text-based instruction.<n>Existing text-to-HOI synthesis methods mainly deploy a direct mapping from texts to object-specific 3D body motions.<n>We propose a novel body pose generation strategy for the text-to-HOI task: infer object-agnostic canonical body action first and then enrich object-specific interaction styles.
• Score: 66.68366281305977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores a cross-modality synthesis task that infers 3D human-object interactions (HOIs) from a given text-based instruction. Existing text-to-HOI synthesis methods mainly deploy a direct mapping from texts to object-specific 3D body motions, which may encounter a performance bottleneck since the huge cross-modality gap. In this paper, we observe that those HOI samples with the same interaction intention toward different targets, e.g., "lift a chair" and "lift a cup", always encapsulate similar action-specific body motion patterns while characterizing different object-specific interaction styles. Thus, learning effective action-specific motion priors and object-specific interaction priors is crucial for a text-to-HOI model and dominates its performances on text-HOI semantic consistency and body-object interaction realism. In light of this, we propose a novel body pose generation strategy for the text-to-HOI task: infer object-agnostic canonical body action first and then enrich object-specific interaction styles. Specifically, the first canonical body action inference stage focuses on learning intra-class shareable body motion priors and mapping given text-based semantics to action-specific canonical 3D body motions. Then, in the object-specific interaction inference stage, we focus on object affordance learning and enrich object-specific interaction styles on an inferred action-specific body motion basis. Extensive experiments verify that our proposed text-to-HOI synthesis system significantly outperforms other SOTA methods on three large-scale datasets with better semantic consistency and interaction realism performances.

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