arXiv subjects

Md Mushfiqur Azam

Publications and source records attributed to Md Mushfiqur Azam.

3 recordsLinked to original sources

arXiv2026

AG-EgoPose: Spatially Anchored Residual Correction with Action Context for Monocular Egocentric 3D Pose Estimation

Monocular egocentric 3D pose estimation is difficult because severe foreshortening, self-occlusion, and a restricted field of view often remove the image evidence needed to recover the camera wearer's body. Temporal context can resolve these ambiguities, but unconstrained fusion may also corrupt joints that are already localized reliably from the current frame. We present AG-EgoPose, an action-guided framework that treats temporal information as a bounded-gate residual correction to a spatial pose estimate. A frozen heatmap network produces joint heatmaps and feature-pyramid features, from which a spatial transformer estimates an independently supervised 3D pose. The heatmaps further guide joint-local pooling of frozen DINOv2 patch tokens, while DINOv2 frame tokens are processed by an Ego4D-pretrained ActionFormer to obtain action-scale temporal context. Each spatial joint token, augmented with its local appearance feature, attends to the temporal sequence and predicts a zero-initialized 3D residual. A per-joint gate, derived from heatmap location and confidence statistics, bounds the residual weight. AG-EgoPose improves over the strongest evaluated baselines by 10.1% PA-MPJPE on EgoPW, and by 9.0% MPJPE and 6.3% PA-MPJPE on SceneEgo after fine-tuning. Code will be made publicly available at https://github.com/

cs.CV↗

arXiv2026

TSR-Ego: Temporally Guided Stereo Refinement Framework for Egocentric 3D Human Pose Estimation

Egocentric 3D human pose estimation from head-mounted stereo cameras is challenging due to fisheye distortion, severe self-occlusion, and frequent truncation of body joints outside the camera field of view. Recent stereo egocentric methods have improved performance through heatmap lifting, stereo correspondence, and transformer-based refinement, but they often rely heavily on frame-local evidence or use temporal information only as auxiliary pose-level context. This limits robustness when current-frame stereo cues are weak, occluded, or ambiguous. We propose TSR-Ego, a temporally guided stereo framework that couples short-term motion evidence with projection-guided feature sampling. The model first enriches dense stereo feature maps using a causal depthwise-separable temporal convolution, allowing past visual evidence to influence the feature space before deformable cross-attention. A single-stage causal stereo decoder then refines learned 3D joint queries through temporal self-attention, joint self-attention, and fisheye deformable stereo cross-attention, using the evolving pose estimate to generate 2D sampling references. Unlike methods that apply temporal reasoning mainly after pose prediction, TSR-Ego uses motion context to shape both the sampled stereo features and the joint representations while preserving online inference without future frames. Experiments on UnrealEgo2 and UnrealEgo-RW show state-of-the-art performance, with especially strong gains on real-world sequences.

cs.CV↗

arXiv2024

A Survey on 3D Egocentric Human Pose Estimation

Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective. It has gained vast popularity in recent years because of its wide range of applications in sectors like XR-technologies, human-computer interaction, and fitness tracking. However, to the best of our knowledge, there is no systematic literature review based on the proposed solutions regarding egocentric 3D human pose estimation. To that end, the aim of this survey paper is to provide an extensive overview of the current state of egocentric pose estimation research. In this paper, we categorize and discuss the popular datasets and the different pose estimation models, highlighting the strengths and weaknesses of different methods by comparative analysis. This survey can be a valuable resource for both researchers and practitioners in the field, offering insights into key concepts and cutting-edge solutions in egocentric pose estimation, its wide-ranging applications, as well as the open problems with future scope.

cs.CV↗