Causal-Flow: Deconfounding Dark Optical Flow Estimation

Dark Optical flow estimation is fundamentally challenged by the entanglement of true motion and degradation-induced confounders in the observed information. Existing paradigms treat this entangled representation holistically, applying enhancement or suppression without explicitly identifying the degradation contribution, thereby incurring an inherent trade-off between preserving true motion and suppressing spurious responses. In this paper, we propose Causal-Flow, a causal decoding framework built on a dual causal design. First, a Causal Intervention Module (CIM) performs front-door adjustment, projecting motion features onto a low-rank subspace spanned by true motion patterns to remove high-dimensional stochastic confounders. Second, a Counterfactual Gating Unit (CGU) estimates degradation-induced spurious motion and derives adaptive spatial and channel gates from causal residuals to enhance true motion and suppress ambiguities. By embedding the CGU into each gate of the GRU-based decoder, Causal-Flow enables single-step decoding without iterative refinement, circumventing error accumulation and achieving state-of-the-art performance on dark-scene optical flow benchmarks.