Fizza Rubab

Fizza Rubab, Yiying Tong, Arun Ross

Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks. At the same time, foundation models that are pretrained on broad vision or vision-language tasks have shown impressive generalization across diverse domains, including biometrics. This raises an important question: Do different DNN models--both domain-specific and foundation models--encode facial identity in similar ways, despite being trained on different datasets, loss functions, and architectures? In this regard, we directly analyze the geometric structure of embedding spaces imputed by different DNN models. Treating embeddings of face images as point clouds, we study whether simple affine transformations can align face representations of one model with another. Our findings reveal surprising cross-model compatibility: low-capacity linear mappings substantially improve cross-model face recognition over unaligned baselines for both face identification and verification tasks. Alignment patterns generalize across datasets and vary systematically across model families, indicating representational convergence in facial identity encoding. These findings have implications for model interoperability, ensemble design, and biometric template security.

Fizza Rubab, Ntumba Elie Nsampi, Martin Balint et al.

Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators.

Fizza Rubab, Yiying Tong

Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry, achieving impressive results in tasks such as shape representation, reconstruction, and learning priors. However, directly editing these representations poses challenges due to the complex relationship between model weights and surface regions they influence. Among such editing tools, sculpting, which allows users to interactively carve or extrude the surface, is a valuable editing operation to the graphics and modeling community. While traditional mesh-based tools like ZBrush facilitate fast and intuitive edits, a comparable toolkit for sculpting neural SDFs is currently lacking. We introduce a framework that enables interactive surface sculpting edits directly on neural implicit representations. Unlike previous works limited to spot edits, our approach allows users to perform stroke-based modifications on the fly, ensuring intuitive shape manipulation without switching representations. By employing tubular neighborhoods to sample strokes and custom brush profiles, we achieve smooth deformations along user-defined curves, providing precise control over the sculpting process. Our method demonstrates that intricate and versatile edits can be made while preserving the smooth nature of implicit representations.