Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint

• URL: http://arxiv.org/abs/2509.24423v1
• Date: Mon, 29 Sep 2025 08:10:41 GMT
• Title: Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint
• Authors: Runmin Zhang, Jialiang Wang, Si-Yuan Cao, Zhu Yu, Junchen Yu, Guangyi Zhang, Hui-Liang Shen,
• Abstract summary: DCFlow is a novel unsupervised cross-modal flow estimation framework.<n>We introduce a decoupled optimization strategy with task-specific supervision to address modality discrepancy and geometric misalignment distinctly.<n>For evaluation, we construct a comprehensive cross-modal flow benchmark by repurposing public datasets.
• Score: 20.46870753632375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents DCFlow, a novel unsupervised cross-modal flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint. Unlike previous approaches that implicitly learn flow estimation solely from appearance similarity, we introduce a decoupled optimization strategy with task-specific supervision to address modality discrepancy and geometric misalignment distinctly. This is achieved by collaboratively training a modality transfer network and a flow estimation network. To enable reliable motion supervision without ground-truth flow, we propose a geometry-aware data synthesis pipeline combined with an outlier-robust loss. Additionally, we introduce a cross-modal consistency constraint to jointly optimize both networks, significantly improving flow prediction accuracy. For evaluation, we construct a comprehensive cross-modal flow benchmark by repurposing public datasets. Experimental results demonstrate that DCFlow can be integrated with various flow estimation networks and achieves state-of-the-art performance among unsupervised approaches.

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