Li, Chen, Chinthani Sugandhika, Yeo Keat Ee, Eric Peh, Hao Zhang, Hong Yang, Deepu Rajan, and Basura Fernando. "IMoRe: Implicit Program-Guided Reasoning for Human Motion QA" In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12987-12996. 2025.
Abstract:
Existing human motion QA methods rely on explicit program execution, where the requirement for manually defined functional modules may limit the scalability and adaptability. To overcome this, we propose an implicit program-guided motion reasoning (IMoRe) framework that unifies reasoning across multiple query types without manually designed modules. Unlike existing implicit reasoning approaches that infer reasoning operations from question words, our model directly conditions on structured program functions, ensuring a more precise execution of reasoning steps. Additionally, we introduce a program-guided reading mechanism, which dynamically selects multi-level motion representations from a pretrained motion Vision Transformer (ViT), capturing both high-level semantics and fine-grained motion cues. The reasoning module iteratively refines memory representations, leveraging structured program functions to extract relevant information for different query types. Our model achieves state-of-the-art performance on Babel-QA and generalizes to a newly constructed motion QA dataset based on HuMMan, demonstrating its adaptability across different motion reasoning datasets. Code and dataset are available at https://github. com/LUNAProject22/IMoRe.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - NRF Fellowship
Grant Reference no. : NRF-NRFF14-2022-0001
This research / project is supported by the Science and Engineering Research Council - Central Research Fund (CRF)
Grant Reference no. :