TAP3D is a new system that reconstructs dense 3D human point clouds from low‑cost thermal array sensors, avoiding the high cost, sparsity, and privacy issues of LiDAR, radar, and depth cameras. It uses a physics‑informed design with a forward thermal model, multi‑primitive estimation for self‑supervised depth recovery, and geometric perspective fusion to suppress interference and separate multiple people. The system, built on a single commodity sensor and evaluated on a 160K‑sample dataset, achieves high accuracy in downstream tasks such as fall detection, indoor tracking, and human mesh recovery.
arXiv:2610.10196v1 Announce Type: new Abstract: LiDAR point clouds of humans are extremely expensive to collect and annotate, thus represent a scarce resource that hinders the development of human an...
The paper introduces a unified framework for multimodal 3D human pose estimation that fuses RGB, LiDAR, and mmWave radar data while incorporating kinematics-based sensor fusion. It presents a black-box subject membership inference attack and a pointwise maximal leakage analysis to assess privacy risks, and proposes a user-level differential privacy method called Action Temporal Stratification to mitigate these risks. The framework is evaluated on the MM-Fi dataset under three experimental protocols, with source code to be released upon acceptance.
arXiv:2608.30521v1 Announce Type: new Abstract: Conventional algebraic triangulation solves 3D human pose estimation (HPE) from multi-view 2D keypoints. The typical approach, decoding 2D keypoints fr...
arXiv:2609.06195v1 Announce Type: new Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds...
arXiv:2608. 07106v1 Announce Type: new Abstract: Deploying three-dimensional deep learning frameworks to low-power embedded processors is bottlenecked by the unstructured nature of spatial data and the resource-intensive distance sorting algorithms often used before neural network inference.
arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.
arXiv:2607. 15868v1 Announce Type: cross Abstract: Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR.
arXiv:2609.32013v2 Announce Type: replace-cross Abstract: We present TriO, a multi-modal unsupervised world model that predicts 4D occupancy, obstacle segmentation, flow and LiDAR. In contrast to pri...
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.
MeshBind introduces an automatic annotation pipeline that generates per-point scene flow for non‑rigid objects such as pedestrians by reconstructing articulated mesh models from six surround cameras and registering them to LiDAR data. The method employs a local‑frame binding technique that attaches each point to a mesh via a per‑triangle tangent‑normal frame, and a three‑layer flow registration pipeline that compensates ego‑vehicle motion, performs box binding, and applies mesh binding to produce full‑scene flow. MeshBind is implemented in the SF‑Studio tool, and the authors release the SUScape‑Flow Benchmark (SFB), a large‑scale dataset with 41k frames of per‑point non‑rigid pedestrian annotations and a comprehensive evaluation protocol, demonstrating that fine‑tuning on SFB reduces endpoint error by 2‑3× and equalizes non‑rigid and rigid EPE.
Physical AI Smart Spaces is the first benchmark that offers large‑scale, multi‑class, multi‑camera 3D perception data for indoor smart spaces. It includes over 280 hours of synchronized 1080p footage from nearly 1,800 cameras in warehouses, hospitals, and retail venues, with automatic annotations for identities, 2D and 3D bounding boxes, camera calibration, and depth. The benchmark spans synthetic generation, appearance augmentation, and real‑world Sim2Real evaluation, and introduces a 3D version of Higher Order Tracking Accuracy (HOTA) for evaluating multi‑class 3D box tracking.
The paper introduces MILO, a framework that uses Large Reconstruction Models (LRMs) to reconstruct detailed 3D human‑object interactions from a single image. By treating the LRM mesh as a geometric scaffold, MILO segments it into human and object parts, fits a parametric body model to the human component, and optionally aligns an object template to the object component. The approach achieves higher reconstruction accuracy than existing baselines across multiple benchmarks and interaction scenarios.