Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging

Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle real-world dynamic distribution shifts after deployment. However, its efficacy is limited by the scarcity and unreliability of supervision signals, leading to error accumulation and catastrophic forgetting. While existing methods predominantly follow a backward-alignment paradigm, constructing weak supervisory surrogates derived from prior knowledge, they struggle with unreliable supervision and evolving distribution shifts. To overcome this, we propose a novel forward-facilitation paradigm through a dynamic style bridging framework. Specifically, we first construct a compact, offline-generated knowledge base of semantically pure class exemplars to provide reliable content. Subsequently, to mitigate generative bias and handle evolving distribution shifts, we propose a multi-level style bridging mechanism. It dynamically transfers current target domain styles to synthetic proxies at the input, statistics, and representation levels. This process yields on-the-fly proxies that are both semantically reliable and stylistically faithful to the target data, which are then used to construct on-demand supervisory signals, effectively enabling stable and discriminative adaptation under continual shifts. Extensive experiments across standard CTTA benchmarks demonstrate consistent and substantial improvements over recent state-of-the-art methods.