arXiv:2607. 13646v1 Announce Type: cross Abstract: Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios.
Human mesh recovery (HMR) aims to recover 3D human meshes from images. Most existing HMR benchmarks and methods focus on either multi-person reconstruction from a single view or single-person reconstruction from multiple views, where the number of subjects and the scene scale are relatively limited.
Ego-Exo4D is a large-scale dataset that offers synchronized egocentric and multi-view exocentric video for applications such as skill learning, procedural activity understanding, and embodied AI. The original dataset only includes sparse 3D human pose annotations, making dense motion reconstruction challenging. To address this, the authors introduce Ego-Exo4D-HM, a new dataset containing 4D human motion reconstructions for the Ego-Exo4D captures, along with a reconstruction pipeline and accompanying code and documentation.
arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
The paper introduces a top‑down approach for multi‑person 3D reconstruction from multiple views, using a unified, instance‑centric human‑aware 3D space. Observations from different cameras are lifted into this shared space where geometry, appearance, and semantic cues are jointly encoded, and a spatial contrastive learning strategy aligns features of the same person across views while separating different individuals. The method then regresses SMPL parameters from 3D tokens in a feed‑forward manner, achieving robust, accurate, and efficient reconstruction even under severe occlusions.
arXiv:2606. 28215v1 Announce Type: cross Abstract: Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs.
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...
arXiv:2603.12789v3 Announce Type: replace Abstract: Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independ...
arXiv:2609.01276v1 Announce Type: new Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
DirtyMoCap is a marker‑layout‑free framework that converts unordered, noisy optical motion capture markers into a fixed set of proxy anchors representing skeletal joints and body surface points. Using a recurrent sliding‑window architecture to track these anchors and a custom differentiable Gauss‑Newton solver to fit the SMPL‑H model, the method learns adaptive observation confidence, smoothness, and prior weights end‑to‑end. Experiments show that DirtyMoCap generalizes across arbitrary marker configurations, outperforms configuration‑specific baselines in joint and vertex accuracy, and achieves up to a 100× speedup over standard PyTorch implementations, enabling the creation of a temporally coherent Kung Fu motion dataset.
Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals.
arXiv:2509.04276v3 Announce Type: replace Abstract: We present a method for modeling articulated objects from sparse images with unknown camera poses. Existing approaches require dense multi-view obs...
arXiv:2601.13913v3 Announce Type: replace Abstract: We consider monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2...