PhasePose: Wavelet Phase Decomposition for Pose Estimation on Low-Resolution Feature Maps

Accurate human pose estimation is commonly associated with restoring a high-resolution feature map. We show that this requirement is not intrinsic to the task. In a controlled degradation study, heatmap regression collapses when its output lattice is reduced from \(64\times48\) to \(8\times6\), whereas coordinate-based decoders remain near their full-resolution accuracy. The remaining obstacle is therefore not simply a small lattice, but how downsampling mixes sampling phases and how a decoder represents localization uncertainty. We propose PhasePose, an upsampling-free top-down estimator that addresses both issues. First, a critically sampled Haar analysis decomposes the four sampling phases of a stride-16 feature into one low-frequency and three directional-detail bands. The low-frequency band is kept fixed, while the three detail bands admit a grouped learnable \(SO(3)\) rotation before being stored as compact correction memory; no dense feature map is reconstructed. Second, a weight-shared spectral corrector represents each SimCC prediction by expectation, entropy, and a DCT-anchored spectrum whose analysis basis can adapt through a zero-start low-rank residual. Entropy can further condition a band-wise prior over the cached phase memory, while skeletal message passing and learned step embeddings support three recurrent gated logit corrections. Feature diagnostics show that the Haar anchor reduces mean inter-band correlation from 0.31 to 0.03, while the subsequently adapted directional-detail bands retain 48.1% of the illustrated response energy. On COCO val2017, PhasePose obtains 73.8 AP with ResNet-50 and 76.7/77.5 AP with CSPNeXt-L at \(256\times192\)/\(384\times288\). It also reaches 56.2 AP from a \(64\times48\) input, 92.5 PCKh on MPII, 74.7 AP on CrowdPose, and 64.5 whole-body AP on COCO-WholeBody.