The Effects of Network Latency on the Peeker's Advantage in First-person Shooter Games

In first-person shooter (FPS) games, the peeker's advantage is the edge the moving peeker gets when battling a stationary defender at a corner due to network latency. However, confirmation of (the size of) this advantage based on network latency and the distance from the corner has not been studied. This paper assesses the peeker's advantage via two user studies both using an open-source FPS game extended to support two-player networking and a custom map. Users play as both peeker and defender with 3 different corner distances and 3 different network latencies.

High-Precision Benchmarks for the Stochastic Rod

We demonstrate a method to calculate high-precision benchmarks for the reflectance and transmittance of a finite rod with a stochastic cross section, assuming that the attenuation law has a known closed form and both the single-scattering albedo and scattering kernel are deterministic. We introduce new 10-digit values for an existing binary-Markov benchmark (including mean and variance), along with several new benchmarks defined for non-Markov binary mixtures and a continuous-fluctuation model featuring gamma stationary statistics.

A Layered, Heterogeneous Reflectance Model for Acquiring and Rendering Human Skin

We introduce a layered, heterogeneous spectral reflectance model for human skin. The model captures the inter-scattering of light among layers, each of which may have an independent set of spatially-varying absorption and scattering parameters. For greater physical accuracy and control, we introduce an infinitesimally thin absorbing layer between scattering layers. To obtain parameters for our model, we use a novel acquisition method that begins with multi-spectral photographs.

Chong Xiang

Chong is a member of the Security and Privacy Research team, focusing on algorithmic protection for AI models and systems. Chong joined NVIDIA in 2025, after earning his PhD from Princeton University in 2024 and his BSE from Shanghai Jiao Tong University in 2019

Imad El Hanafi

Interested in distributed systems for large scale training and inference.

https://imadelh.gitlab.io 

Eugene d'Eon

Eugene d'Eon is a Principal Research Scientist at NVIDIA, where he specializes in real-time rendering technologies. His career includes significant contributions during his initial tenure at NVIDIA, where he published influential works on real-time skin rendering. Subsequently, at Weta Digital, he advanced research in the appearance modeling of tissue, hair, and rough surfaces.

CorrFill: Enhancing Faithfulness in Reference-based Inpainting with Correspondence Guidance in Diffusion Models

In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state. The advancement of diffusion models, particularly Stable Diffusion, allows for simple formulations in this task. However, existing diffusion-based methods often lack explicit constraints on the correlation between the reference and damaged images, resulting in lower faithfulness to the reference images in the inpainting results.