Esteban Walter Gonzalez Clua

Isaac Fiuza Vieira, Kathya Silvia Collazos Linares, Esteban Walter Gonzalez Clua et al.

Procedural Content Generation (PCG) has become an essential technique in game development due to its ability to reduce production time and cost while increasing replayability and variety. However, when not aligned with level design principles, PCG can lead to incoherent spatial structures and poor gameplay experiences. Objective: This work proposes a PCG method guided by level design principles to generate structured indoor environments - such as houses, mansions, and dungeons - aiming to ensure both architectural coherence and navigability. Methodology: The method is divided into three main stages: segmentation of the space using Binary Space Partitioning (BSP); logical connection of rooms based on graph traversal to prevent redundant links; and a post-processing stage responsible for cleaning structural artifacts and improving visual cohesion. The methodology allows parameterization of room area and shape, with randomness controlled via seeds for reproducibility. Results: Two experiments were conducted. The first demonstrated the flexibility of the methodology under different seeds and parameter configurations. The second evaluated the navigability of generated maps by verifying connectivity using Breadth-First Search (BFS). In this test, 100,000 maps were generated, and with suitable parameters, over 91% of them achieved complete connectivity.

Bruno Augusto Dorta Marques, Esteban Walter Gonzalez Clua, Anselmo Antunes Montenegro et al.

The representation of consistent mixed reality (XR) environments requires adequate real and virtual illumination composition in real-time. Estimating the lighting of a real scenario is still a challenge. Due to the ill-posed nature of the problem, classical inverse-rendering techniques tackle the problem for simple lighting setups. However, those assumptions do not satisfy the current state-of-art in computer graphics and XR applications. While many recent works solve the problem using machine learning techniques to estimate the environment light and scene's materials, most of them are limited to geometry or previous knowledge. This paper presents a CNN-based model to estimate complex lighting for mixed reality environments with no previous information about the scene. We model the environment illumination using a set of spherical harmonics (SH) environment lighting, capable of efficiently represent area lighting. We propose a new CNN architecture that inputs an RGB image and recognizes, in real-time, the environment lighting. Unlike previous CNN-based lighting estimation methods, we propose using a highly optimized deep neural network architecture, with a reduced number of parameters, that can learn high complex lighting scenarios from real-world high-dynamic-range (HDR) environment images. We show in the experiments that the CNN architecture can predict the environment lighting with an average mean squared error (MSE) of \num{7.85e-04} when comparing SH lighting coefficients. We validate our model in a variety of mixed reality scenarios. Furthermore, we present qualitative results comparing relights of real-world scenes.