Bringing a Personal Point of View: Evaluating Dynamic 3D Gaussian Splatting for Egocentric Scene Reconstruction

Egocentric video provides a unique view into human perception and interaction, with growing relevance for augmented reality, robotics, and assistive technologies. However, rapid camera motion and complex scene dynamics pose major challenges for 3D reconstruction from this perspective. While 3D Gaussian Splatting (3DGS) has become a state-of-the-art method for efficient, high-quality novel view synthesis, variants, that focus on reconstructing dynamic scenes from monocular video are rarely evaluated on egocentric video. It remains unclear whether existing models generalize to this setting or if egocentric-specific solutions are needed. In this work, we evaluate dynamic monocular 3DGS models on egocentric and exocentric video using paired ego-exo recordings from the EgoExo4D dataset. We find that reconstruction quality is consistently lower in egocentric views. Analysis reveals that the difference in reconstruction quality, measured in peak signal-to-noise ratio, stems from the reconstruction of static, not dynamic, content. Our findings underscore current limitations and motivate the development of egocentric-specific approaches, while also highlighting the value of separately evaluating static and dynamic regions of a video.

GRLib: An Open-Source Hand Gesture Detection and Recognition Python Library

Hand gesture recognition systems provide a natural way for humans to interact with computer systems. Although various algorithms have been designed for this task, a host of external conditions, such as poor lighting or distance from the camera, make it difficult to create an algorithm that performs well across a range of environments. In this work, we present GRLib: an open-source Python library able to detect and classify static and dynamic hand gestures. Moreover, the library can be trained on existing data for improved classification robustness. The proposed solution utilizes a feed from an RGB camera. The retrieved frames are then subjected to data augmentation and passed on to MediaPipe Hands to perform hand landmark detection. The landmarks are then classified into their respective gesture class. The library supports dynamic hand gestures through trajectories and keyframe extraction. It was found that the library outperforms another publicly available HGR system - MediaPipe Solutions, on three diverse, real-world datasets. The library is available at https://github.com/mikhail-vlasenko/grlib and can be installed with pip.

Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models

In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of-the-art deep learning models requires access to large amounts of data and computational power. However, gathering such data is challenging and computational resources might be limited. This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power. We measure data efficiency by training each model on a subset of the training set. We find that TemporalMaxer outperforms other models in data-limited settings. Furthermore, we recommend TriDet when training time is limited. To test the efficiency of the models during inference, we pass videos of different lengths through each model. We find that TemporalMaxer requires the least computational resources, likely due to its simple architecture.