Abstract Realistic hair rendering remains a significant challenge in computer graphics due to the intricate microstructure of hair fibers and their anisotropic scattering properties, which make them highly sensitive to noise. Although recent advancements in image‐space and 3D‐space denoising and antialiasing techniques have facilitated real‐time rendering in simple scenes, existing methods still struggle with excessive blurring and artifacts, particularly in fine hair details such as flyaway strands. These issues arise because current techniques often fail to preserve sub‐pixel continuity and lack directional sensitivity in the filtering process. To address these limitations, we introduce a novel real‐time hair filtering technique that effectively reconstructs fine fiber details while suppressing noise. Our method improves visual quality by maintaining strand‐level details and ensuring computational efficiency, making it well‐suited for real‐time applications in video games and virtual reality (VR) and augmented reality (AR) environments.

Abstract We present a real‐time strand‐based rendering framework that ensures seamless transitions between different level‐of‐detail (LoD) while maintaining a consistent appearance. We first introduce an aggregated BCSDF model to accurately capture both single and multiple scattering within the cluster for hairs and fibers. Building upon this, we further introduce a LoD framework for hair rendering that dynamically, adaptively, and independently replaces clusters of individual hairs with thick strands based on their projected screen widths. Through tests on diverse hairstyles with various hair colors and animation, as well as knit patches, our framework closely replicates the appearance of multiple‐scattered full geometries at various viewing distances, achieving up to a 13× speedup.

The knit structure consists of interlocked yarns, with each yarn comprising multiple plies comprising tens to hundreds of twisted fibers. This intricate geometry and the large number of geometric primitives present substantial challenges for achieving high-fidelity simulation and rendering in real-time applications. In this work, we introduce the first real-time framework that takes an animated stitch mesh as input and enhances it with yarn-level simulation and fiber-level rendering. Our approach relies on a knot-based representation to model interlocked yarn contacts. The knot positions are interpolated from the underlying mesh, and associated yarn control points are optimized using a physically inspired energy formulation, which is solved through a GPU-based Gauss-Newton scheme for real-time performance. The optimized control points are sent to the GPU rasterization pipeline and rendered as yarns with fiber-level details. In real-time rendering, we introduce several decomposition strategies to enable realistic lighting effects on complex knit structures, even under environmental lighting, while maintaining computational and memory efficiency. Our simulation faithfully reproduces yarn-level structures under deformations, e.g., stretching and shearing, capturing interlocked yarn behaviors. The rendering pipeline achieves near-ground-truth visual quality while being 120,000× faster than path tracing reference with fiber-level geometries. The whole system provides real-time performance and has been evaluated through various application scenarios, including knit simulation for small patches and full garments and yarn-level relaxation in the design pipeline.

Hair cards remain a widely used representation for hair modeling in real-time applications, offering a practical trade-off between visual fidelity, memory usage, and performance. However, generating high-quality hair card models remains a challenging and labor-intensive task. This work presents an automated pipeline for converting strand-based hair models into hair card models with a limited number of cards and textures while preserving the hairstyle appearance. Our key idea is a novel differentiable representation where each strand is encoded as a projected 2D curve in the texture space, which enables end-to-end optimization with differentiable rendering while respecting the structures of the hair geometry. Based on this representation, we develop a novel algorithm pipeline, where we first cluster hair strands into initial hair cards and project the strands into the texture space. We then conduct a two-stage optimization, where our first stage optimizes the orientation of each hair card separately, and after strand projection, our second stage conducts joint optimization over the entire hair card model for fine-tuning. Our method is evaluated on a range of hairstyles, including straight, wavy, curly, and coily hair. To capture the appearance of short or coily hair, our method comes with support for hair caps and cross-card.

Creating an appearance-preserving level-of-detail (LoD) representation for arbitrary 3D scenes is a challenging problem. The appearance of a scene is an intricate combination of both geometry and material models and is further complicated by correlation due to the spatial configuration of scene elements. We present a novel volumetric representation for the aggregated appearance of complex scenes and a pipeline for LoD generation and rendering. The core of our representation is the Aggregated Bidirectional Scattering Distribution Function (ABSDF) that summarizes the far-field appearance of all surfaces inside a voxel. We propose a closed-form factorization of the ABSDF that accounts for spatially varying and orientation-varying material parameters. We tackle the challenge of capturing the correlation existing locally within a voxel and globally across different parts of the scene. Our method faithfully reproduces appearance and achieves higher quality than existing scene filtering methods. The memory footprint and rendering cost of our representation are decoupled from the original scene complexity.

Abstract Real‐time global illumination is a highly desirable yet challenging task in computer graphics. Existing works well solving this problem are mostly based on some kind of precomputed data (caches), while the final results depend significantly on the quality of the caches. In this paper, we propose a learning‐based pipeline that can reproduce a wide range of complex light transport phenomena, including high‐frequency glossy interreflection, at any viewpoint in real time (> 90 frames per‐second), using information from imperfect caches stored at the barycentre of every triangle in a 3D scene. These caches are generated at a precomputation stage by a physically‐based offline renderer at a low sampling rate (e.g., 32 samples per‐pixel) and a low image resolution (e.g., 64×16). At runtime, a deep radiance reconstruction method based on a dedicated neural network is then involved to reconstruct a high‐quality radiance map of full global illumination at any viewpoint from these imperfect caches, without introducing noise and aliasing artifacts. To further improve the reconstruction accuracy, a new feature fusion strategy is designed in the network to better exploit useful contents from cheap G‐buffers generated at runtime. The proposed framework ensures high‐quality rendering of images for moderate‐sized scenes with full global illumination effects, at the cost of reasonable precomputation time. We demonstrate the effectiveness and efficiency of the proposed pipeline by comparing it with alternative strategies, including real‐time path tracing and precomputed radiance transfer.