S2Gest: Split-Scan State Space Models for Dynamic Hand Gesture Recognition

Dynamic Hand Gesture Recognition is essential for naturalhuman-computer interaction but faces challenges in continuous local ex-ecution on consumer-grade hardware due to strict computational andlatency constraints. Existing solutions fail to reconcile the high MemoryAccess Cost (MAC) and dynamic footprint of Video Transformers with thelimited temporal modeling capacity of lightweight CNNs. To address theselimitations, we propose the Split-Scan Gesture Network (S2Gest), whichrethinks temporal modeling by leveraging continuous state-space evolutionto represent discrete sequential inputs. We introduce the Split-Scan Block(S2-Block), employing a novel tube-wise scanning strategy that partitionsfeature channels to process opposing temporal directions simultaneously.By structurally partitioning feature channels to capture complementaryforward and backward dynamics, this design achieves global bidirectionalmodeling with linear-time complexity and without introducing additionalparameters, effectively overcoming the causal unidirectionality inherent instandard State Space Models (SSMs). Evaluated across three benchmarks,the scalable S2Gest model family establishes a new state-of-the-art forlightweight architectures, matching the performance of large-scale base-lines with a sub-3M parameter footprint while consistently exceeding 100clips/s on consumer-grade GPUs. Code and weights are publicly availableat https://github.com/Chen-Ke-Fan/S2Gest.