Gait recognition using 3D point clouds offers resilience to appearance variations but faces challenges in modeling sparse, noisy, and temporally misaligned sequences. We propose the Pristine Learning Framework, designed to extract noise-invariant gait representations from 3D point cloud data. At its core, a (2+1)D convolution network separates spatial and temporal feature learning, reducing model complexity while improving resilience under limited data. A key innovation is the Pristine Feature Vector, a dynamically refined representation of the intrinsic gait cycle learned by fusing features from N subjects mitigating any individual biases and environmental noise. This vector is continuously updated through a self-attention mechanism that enhances discriminative biomechanical regions and critical gait phases while suppressing transient noise. Additionally, a cross-attention module aligns gait features with the pristine vector, further improving generalization across subjects. Experimental evaluations on benchmark dataset demonstrate state-of-the-art accuracy, with a \(5\%\) improvement in Rank-1 accuracy over existing methods. This work advances the feasibility of deployable, privacy-preserving gait recognition systems by addressing critical challenges in 3D spatiotemporal modeling and noise-invariant feature learning.