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arXiv:2605.14854v3 Announce Type: replace-cross Abstract: Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evide...
Reconstructing humans and their surrounding environments in a globally consistent 4D space is essential for comprehensive perception. However, prior works typically assume single-view inputs or decouple humans, scenes, and cameras, making them unable to recover coherent geometry, stable motion, and physically aligned trajectories.
arXiv:2603.14528v3 Announce Type: replace Abstract: In recent years, 3D visual foundation models, pioneered by pointmap-based approaches such as DUSt3R, have attracted a lot of interest, achieving im...
arXiv:2509.04600v2 Announce Type: replace Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
The paper introduces a top‑down framework for accurately locating athletes in metric world coordinates using a single calibrated broadcast frame. It presents three main contributions: a Boundary‑Aware Adaptive Tiling method that expands tile boundaries to avoid splitting athletes across tiles, a specialized two‑keypoint estimator based on RTMPose‑X for pelvis and ground projection points, and a deterministic lift of 2D projections into 3D world coordinates via camera‑calibrated ray casting. The approach achieves a LocSim score of 97.44 and an mAP of 0.9128, surpassing the baseline by over 21 % on a public test set.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.