Manuel Meier

Jiaxi Jiang, Paul Streli, Manuel Meier et al.

Full-body egocentric pose estimation from head and hand poses alone has become an active area of research to power articulate avatar representations on headset-based platforms. However, existing methods over-rely on the indoor motion-capture spaces in which datasets were recorded, while simultaneously assuming continuous joint motion capture and uniform body dimensions. We propose EgoPoser to overcome these limitations with four main contributions. 1) EgoPoser robustly models body pose from intermittent hand position and orientation tracking only when inside a headset's field of view. 2) We rethink input representations for headset-based ego-pose estimation and introduce a novel global motion decomposition method that predicts full-body pose independent of global positions. 3) We enhance pose estimation by capturing longer motion time series through an efficient SlowFast module design that maintains computational efficiency. 4) EgoPoser generalizes across various body shapes for different users. We experimentally evaluate our method and show that it outperforms state-of-the-art methods both qualitatively and quantitatively while maintaining a high inference speed of over 600fps. EgoPoser establishes a robust baseline for future work where full-body pose estimation no longer needs to rely on outside-in capture and can scale to large-scale and unseen environments.

Björn Braun, Rayan Armani, Manuel Meier et al.

Egocentric vision systems aim to understand the spatial surroundings and the wearer's behavior inside it, including motions, activities, and interactions. We argue that egocentric systems must additionally detect physiological states to capture a person's attention and situational responses, which are critical for context-aware behavior modeling. In this paper, we propose egoPPG, a novel vision task for egocentric systems to recover a person's cardiac activity to aid downstream vision tasks. We introduce PulseFormer, a method to extract heart rate as a key indicator of physiological state from the eye tracking cameras on unmodified egocentric vision systems. PulseFormer continuously estimates the photoplethysmogram (PPG) from areas around the eyes and fuses motion cues from the headset's inertial measurement unit to track HR values. We demonstrate egoPPG's downstream benefit for a key task on EgoExo4D, an existing egocentric dataset for which we find PulseFormer's estimates of HR to improve proficiency estimation by 14%. To train and validate PulseFormer, we collected a dataset of 13+ hours of eye tracking videos from Project Aria and contact-based PPG signals as well as an electrocardiogram (ECG) for ground-truth HR values. Similar to EgoExo4D, 25 participants performed diverse everyday activities such as office work, cooking, dancing, and exercising, which induced significant natural motion and HR variation (44-164 bpm). Our model robustly estimates HR (MAE=7.67 bpm) and captures patterns (r=0.85). Our results show how egocentric systems may unify environmental and physiological tracking to better understand users and that egoPPG as a complementary task provides meaningful augmentations for existing datasets and tasks. We release our code, dataset, and HR augmentations for EgoExo4D to inspire research on physiology-aware egocentric tasks.

Manuel Meier, Berken Utku Demirel, Christian Holz

Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such as temperature and ambient light. These and other factors can significantly impact and decrease HR prediction reliability. In this paper, we show that state-of-the-art HR estimation methods struggle when processing \emph{representative} data from everyday activities in outdoor environments, likely because they rely on existing datasets that captured controlled conditions. We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours, captured from four wearable sensors, each worn at a different location on the body, totaling 216\,hours. Our recordings include accelerometer, temperature, and altitude data, as well as a synchronized Lead I-based electrocardiogram for ground-truth HR references. Participants completed a round trip from Zurich to Jungfraujoch, a tall mountain in Switzerland over the course of one day. The trip included outdoor and indoor activities such as walking, hiking, stair climbing, eating, drinking, and resting at various temperatures and altitudes (up to 3,571\,m above sea level) as well as using cars, trains, cable cars, and lifts for transport -- all of which impacted participants' physiological dynamics. We also present a novel method that estimates HR values more robustly in such real-world scenarios than existing baselines.