Human motion estimation models typically assume a fixed number of input frames, making them sensitive to variations in frame rate and leading to inconsistent motion predictions across different temporal resolutions. This limitation arises because input frame rates inherently determine the temporal granularity of motion capture, causing discrepancies when models trained on a specific frame rate encounter different sampling frequencies. To address this challenge, we propose MBTI (Masked Blending Transformers with Implicit Positional Encoding), a frame rate-agnostic human motion estimation framework designed to maintain temporal consistency across varying input frame rates. Our approach leverages a masked autoencoder (MAE) architecture with masked token blending, which aligns input tokens with a predefined high-reference frame rate, ensuring a standardized temporal representation. Additionally, we introduce implicit positional encoding, which encodes absolute time information using neural implicit functions, enabling more natural motion reconstruction beyond discrete sequence indexing. By reconstructing motion at a high reference frame rate and optional downsampling, MBTI ensures both frame rate generalization and temporal consistency. To comprehensively evaluate MBTI, we introduce EMDB-FPS, an augmented benchmark designed to assess motion estimation robustness across multiple frame rates in both local and global motion estimation tasks. To further assess MBTI’s robustness, we introduce the Motion Consistency across Frame rates (MCF), a novel metric to quantify the deviation of motion predictions across different input frame rates. Our results demonstrate that MBTI outperforms state-of-the-art methods in both motion accuracy and temporal consistency, achieving the most stable and consistent motion predictions across varying frame rates.