Understanding human motion from video is crucial for applications such as pose estimation, mesh recovery, and action recognition. While state-of-the-art methods predominantly rely on Transformer-based architectures, these approaches have limitations in practical scenarios. They are notably slower when processing a continuous stream of video frames in real time and do not adapt to new frame rates. Given these challenges, we propose an attention free spatiotemporal model for human motion understanding, building upon recent advancements diagonal state space models. Our model performs comparably…
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