PAWS: Perception of Articulation in the Wild at Scale from Egocentric Videos

Articulation perception aims to recover the motion and structure of articulated objects (e.g., drawers and cupboards), and is fundamental to 3D scene understanding in robotics, simulation, and animation. Existing learning-based methods rely heavily on supervised training with high-quality 3D data and manual annotations, limiting scalability and diversity. To address this limitation, we propose PAWS, a method that directly extracts object articulations from hand-object interactions in large-scale in-the-wild egocentric videos. We evaluate our method on the public data sets, including HD-EPIC and Arti4D data sets, achieving significant improvements over baselines. We further demonstrate that the extracted articulations benefit downstream tasks, including fine-tuning 3D articulation prediction models and enabling robot manipulation. See the project website at https://aaltoml.github.io/PAWS/.

Robust Localization in OFDM-Based Massive MIMO through Phase Offset Calibration

Accurate localization in Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) systems depends critically on phase coherence across subcarriers and antennas. However, practical systems suffer from frequency-dependent and (spatial) antenna-dependent phase offsets, degrading localization accuracy. This paper analytically studies the impact of phase incoherence on localization performance under a static User Equipment (UE) and Line-of-Sight (LoS) scenario. We use two complementary tools. First, we derive the Cramér-Rao Lower Bound (CRLB) to quantify the theoretical limits under phase offsets. Then, we develop a Spatial Ambiguity Function (SAF)-based model to characterize ambiguity patterns. Simulation results reveal that spatial phase offsets severely degrade localization performance, while frequency phase offsets have a minor effect in the considered system configuration. To address this, we propose a robust Channel State Information (CSI) calibration framework and validate it using real-world measurements from a practical massive MIMO testbed. The experimental results confirm that the proposed calibration framework significantly improves the localization Root Mean Squared Error (RMSE) from 5 m to 1.2 cm, aligning well with the theoretical predictions.

BS-Breath: Respiration Sensing with Cell-free Massive MIMO

This paper demonstrates the feasibility of respiration pattern estimation utilizing a communication-centric cellfree massive MIMO OFDM Base Station (BS). The sensing target is typically positioned near the User Equipment (UE), which transmits uplink pilots to the BS. Our results demonstrate the potential of massive MIMO systems for accurate and reliable vital sign estimation. Initially, we adopt a single antenna sensing solution that combines multiple subcarriers and a breathing projection to align the 2D complex breathing pattern to a single displacement dimension. Then, Weighted Antenna Combining (WAC) aggregates the 1D breathing signals from multiple antennas. The results demonstrate that the combination of space-frequency resources specifically in terms of subcarriers and antennas yields higher accuracy than using only a single antenna or subcarrier. Our results significantly improved respiration estimation accuracy by using multiple subcarriers and antennas. With WAC, we achieved an average correlation of 0.8 with ground truth data, compared to 0.6 for single antenna or subcarrier methods, a 0.2 correlation increase. Moreover, the system produced perfect breathing rate estimates. These findings suggest that the limited bandwidth (18 MHz in the testbed) can be effectively compensated by utilizing spatial resources, such as distributed antennas.

COST INTERACT Whitepaper on Signal Processing for Communications, Localization, and Intergrated Sensing and Communication

The upcoming next generation of wireless communication is anticipated to revolutionize the conventional functionalities of the network by adding sensing and localization capabilities, low-power communication, wireless brain computer interactions, massive robotics and autonomous systems connection. Furthermore, the key performance indicators expected for the 6G of mobile communications promise challenging operating conditions, such as user data rates of 1 Tbps, end-to-end latency of less than 1 ms, and vehicle speeds of 1000 km per hour. This evolution needs new techniques, not only to improve communications, but also to provide localization and sensing with an efficient use of the radio resources. The goal of INTERACT Working Group 2 is to design novel physical layer technologies that can meet these KPI, by combining the data information from statistical learning with the theoretical knowledge of the transmitted signal structure. Waveforms and coding, advanced multiple-input multiple-output and all the required signal processing, in sub-6-GHz, millimeter-wave bands and upper-mid-band, are considered while aiming at designing these new communications, positioning and localization techniques. This White Paper summarizes our main approaches and contributions.

User-Movement-Robust Virtual Reality Through Dual-Beam Reception in mmWave Networks

Utilizing the mmWave band can potentially achieve the high data rate needed for realistic and seamless interaction within a virtual reality (VR) application. To this end, beamforming in both the access point (AP) and head-mounted display (HMD) sides is necessary. The main challenge in this use case is the specific and highly dynamic user movement, which causes beam misalignment, degrading the received signal level and potentially leading to outages. This study examines mmWave-based coordinated multi-point networks for VR applications, where two or multiple APs cooperatively transmit the signals to an HMD for connectivity diversity. Instead of using omnireception, we propose dual-beam reception based on the analog beamforming at the HMD, enhancing the receive beamforming gain towards serving APs while achieving diversity. Evaluation using actual HMD movement data demonstrates the effectiveness of our approach, showcasing a reduction in outage rates of up to 13% compared to quasi-omnidirectional reception with two serving APs, and a 17% decrease compared to steerable single-beam reception with a serving AP. Widening the separation angle between two APs can further reduce outage rates due to head rotation as rotations can still be tracked using the steerable multi-beam, albeit at the expense of received signal levels reduction during the non-outage period.

Holistic Understanding of 3D Scenes as Universal Scene Description

3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI. Providing a solution to these applications requires a multifaceted approach that covers scene-centric, object-centric, as well as interaction-centric capabilities. While there exist numerous datasets approaching the former two problems, the task of understanding interactable and articulated objects is underrepresented and only partly covered by current works. In this work, we address this shortcoming and introduce (1) an expertly curated dataset in the Universal Scene Description (USD) format, featuring high-quality manual annotations, for instance, segmentation and articulation on 280 indoor scenes; (2) a learning-based model together with a novel baseline capable of predicting part segmentation along with a full specification of motion attributes, including motion type, articulated and interactable parts, and motion parameters; (3) a benchmark serving to compare upcoming methods for the task at hand. Overall, our dataset provides 8 types of annotations - object and part segmentations, motion types, movable and interactable parts, motion parameters, connectivity, and object mass annotations. With its broad and high-quality annotations, the data provides the basis for holistic 3D scene understanding models. All data is provided in the USD format, allowing interoperability and easy integration with downstream tasks. We provide open access to our dataset, benchmark, and method's source code.

Measurement-based Characterization of ISAC Channels with Distributed Beamforming at Dual mmWave Bands and with Human Body Scattering and Blockage

In this paper, we introduce our millimeter-wave (mmWave) radio channel measurement for integrated sensing and communication (ISAC) scenarios with distributed links at dual bands in an indoor cavity; we also characterize the channel in delay and azimuth-angular domains for the scenarios with the presence of 1 person with varying locations and facing orientations. In our setting of distributed links with two transmitters and two receivers where each transceiver operates at two bands, we can measure two links whose each transmitter faces to one receiver and thus capable of line-of-sight (LOS) communication; these two links have crossing Fresnel zones. We have another two links capable of capturing the reflectivity from the target presenting in the test area (as well as the background). The numerical results in this paper focus on analyzing the channel with the presence of one person. It is evident that not only the human location, but also the human facing orientation, shall be taken into account when modeling the ISAC channel.

SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs

We introduce a novel problem, i.e., the localization of an input image within a multi-modal reference map represented by a database of 3D scene graphs. These graphs comprise multiple modalities, including object-level point clouds, images, attributes, and relationships between objects, offering a lightweight and efficient alternative to conventional methods that rely on extensive image databases. Given the available modalities, the proposed method SceneGraphLoc learns a fixed-sized embedding for each node (i.e., representing an object instance) in the scene graph, enabling effective matching with the objects visible in the input query image. This strategy significantly outperforms other cross-modal methods, even without incorporating images into the map embeddings. When images are leveraged, SceneGraphLoc achieves performance close to that of state-of-the-art techniques depending on large image databases, while requiring three orders-of-magnitude less storage and operating orders-of-magnitude faster. The code will be made public.

Vital Signs Estimation Using a 26 GHz Multi-Beam Communication Testbed

This paper presents a novel pipeline for vital sign monitoring using a 26 GHz multi-beam communication testbed. In context of Joint Communication and Sensing (JCAS), the advanced communication capability at millimeter-wave bands is comparable to the radio resource of radars and is promising to sense the surrounding environment. Being able to communicate and sense the vital sign of humans present in the environment will enable new vertical services of telecommunication, i.e., remote health monitoring. The proposed processing pipeline leverages spatially orthogonal beams to estimate the vital sign - breath rate and heart rate - of single and multiple persons in static scenarios from the raw Channel State Information samples. We consider both monostatic and bistatic sensing scenarios. For monostatic scenario, we employ the phase time-frequency calibration and Discrete Wavelet Transform to improve the performance compared to the conventional Fast Fourier Transform based methods. For bistatic scenario, we use K-means clustering algorithm to extract multi-person vital signs due to the distinct frequency-domain signal feature between single and multi-person scenarios. The results show that the estimated breath rate and heart rate reach below 2 beats per minute (bpm) error compared to the reference captured by on-body sensor for the single-person monostatic sensing scenario with body-transceiver distance up to 2 m, and the two-person bistatic sensing scenario with BS-UE distance up to 4 m. The presented work does not optimize the OFDM waveform parameters for sensing; it demonstrates a promising JCAS proof-of-concept in contact-free vital sign monitoring using mmWave multi-beam communication systems.

Volumetric Semantically Consistent 3D Panoptic Mapping

We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments. The proposed approach is based on a Voxel-TSDF representation used in recent algorithms. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. Further improvements are achieved by graph optimization-based semantic labeling and instance refinement. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics. We also highlight a downfall in the evaluation of recent studies: using the ground truth trajectory as input instead of a SLAM-estimated one substantially affects the accuracy, creating a large gap between the reported results and the actual performance on real-world data.