Daniele Agostinelli | Searcharxiv

arXiv2026

Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation

Appearance-based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs). These models are accurate, but computationally expensive and act as "black boxes", offering little interpretability. Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities remain underexplored in modern benchmarks. In this study, we conduct a comprehensive evaluation of landmark-based gaze estimation. We introduce a standardized pipeline to extract and normalize landmarks from three large-scale datasets (Gaze360, ETH-XGaze, and GazeGene) and train lightweight regression models, specifically Extreme Gradient Boosted trees and two neural architectures: a holistic Multi-Layer Perceptron (MLP) and a siamese MLP designed to capture binocular geometry. We find that landmark-based models exhibit lower performance in within-domain evaluation, likely due to noise introduced into the datasets by the landmark detector. Nevertheless, in cross-domain evaluation, the proposed MLP architectures show generalization capabilities comparable to those of ResNet18 baselines. These findings suggest that sparse geometric features encode sufficient information for robust gaze estimation, paving the way for efficient, interpretable, and privacy-friendly edge applications. The source code and generated landmark-based datasets are available at https://github.com/daniele-agostinelli/LandmarkGaze.git.

cs.CV↗

arXiv2023

Energetics of Cytoskeletal Gel Contraction

Cytoskeletal gels are prototyped to reproduce the mechanical contraction of the cytoskeleton in-vitro. They are composed of a polymer network (backbone), swollen by the presence of a liquid solvent, and active molecules (molecular motors, MMs) that transduce chemical energy into the mechanical work of contraction. These motors attach to the polymer chains to shorten them and/or act as dynamic crosslinks, thereby constraining the thermal fluctuation of the chains. We describe both mechanisms thermodynamically as a microstructural reconfiguration, where the backbone stiffens to motivate solvent (out)flow and accommodate contraction. Via simple steady-state energetic analysis, under the simplest case of isotropic contraction, we quantify the mechanical energy required to achieve contraction as a function of polymer chain density and molecular motor density. We identify two limit cases, (fm) fast MM activation for which MMs provide all the available mechanical energy instantaneously and leave the polymer in a stiffened state, i.e. their activity occurs at a time scale that is much smaller than solvent diffusion, and (sm) slow MM activation for which the MM activation timescale is much longer. To achieve the same final contracted state, fm requires the largest amount of work per unit reference volume, while sm requires the least. For all intermediate cases where the timescale of MM activation is comparable with that of solvent flow, the required work ranges between the two cases. We provide all these quantities as a function of chain density and MM density. Finally, we compare our results with experiments and observe good agreement.

cond-mat.soft↗