We propose a complete pipeline for modeling and rendering realistic bird feathers from a single photograph, achieving both high visual fidelity and practical efficiency. Given a single input image of a feather, our approach extracts the feather's shaft curve, outline, and albedo, then reconstructs a compact hierarchical representation in a planar/curve (UV) domain. This representation encodes fine barb and barbule details procedurally, enabling continuous multiscale rendering with correct self-shadowing and masking. We analyze the appearance phenomena of different feathers and propose a new feather scattering model for non-iridescent feathers (e.g., parrot feathers), while introducing an additional sheen lobe to capture the distinctive fluffy rim-lighting effect. Our pipeline produces consistent, realistic results under arbitrary lighting and viewing conditions, and achieves real-time performance with a minimal memory footprint (0.02% of explicit-fiber geometry models), making it a practical solution for digital feather rendering without compromising realism.

The theory of light transport on Gaussian process implicit surface (GPIS) provides a unified framework for rendering surfaces, participating media, and the intermediate spectrum. However, previous approaches rely on brute-force ray marching for surface intersections, requiring full noise evaluations at each marching point, whether using multivariate Gaussian sampling or sparse convolution noise approximation. This imposes a severe limitation on the rendering efficiency. In this paper, we derive bounds to significantly reduce the total number of full noise evaluations, leading to efficient ray marching for ray-surface intersections. We introduce stratified Bernoulli impulses, enabling a fast point-level bound for individual realizations to replace unnecessary full noise evaluations. To further reduce the number of point-level bound evaluations, we propose a region-level bound, leveraging a spatial acceleration structure to prune probabilistically empty regions, thereby avoiding unnecessary marching points in advance. By combining these two bounds, our bounded ray marching accelerates ray-surface intersections in GPIS, and consequently significantly improves overall GPIS rendering efficiency. Code for this paper are at https://github.com/Cchen-77/bounded-gpis.

In this paper, we propose an efficient method for hair and fur rendering that approximates multiple scattering as volumetric light transport, achieving near path-traced visual fidelity at a drastically reduced cost. Multiple scattering among hair fibers is crucial for a realistic appearance, especially for light-colored hair, but it is extremely expensive to simulate directly. Prior approximations, such as dual scattering and fur BSSRDF, improve performance but often fail for dense hair/fur, yielding an overly dry or blurred appearance. Unlike previous volumetric hair rendering solutions, we treat dense hair/fur as a highly anisotropic participating medium to capture soft volumetric illumination, while still using explicit fiber geometry for direct lighting to preserve fine details. We accumulate hair fiber coverage in screen space and stochastically sample a single-scattering event to approximate higher-order scattering. Our method reproduces the rich appearance of hair and fur resulting from multiple scattering, while running about 7–10× faster than path tracing, making it suitable for use in production. We also demonstrate that our approach is robust under a variety of lighting conditions.

Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, high-fidelity novel view synthesis. However, rendering each frame independently in a video sequence leads to redundant computations, especially when adjacent frames share significant visual overlaps. This inefficiency is particularly problematic in VR applications, where high frame rates and stereoscopic rendering amplify the per-frame cost. Existing frame interpolation or reuse strategies typically rely on image-domain information and are thus not directly applicable to 3DGS rendering, which is fundamentally point-based. To address this runtime inefficiency, we propose GSReuse, a lightweight and drop-in accelerator that speeds up 3DGS rendering by reusing computations across consecutive frames. GSReuse operates in screen space and introduces only minimal modifications to existing 3DGS rendering pipelines. It also eliminates the need for retraining scene representations. Given the rendered image, depth map, and camera parameters of the current frame, GSReuse estimates reliable Gaussian splatting motion vectors for all pixels and warps reusable contents to the new view. A tile-based filtering and masking strategy is then applied to determine which regions can be safely reused, allowing the 3DGS renderer to skip redundant rendering operations. We evaluate GSReuse on multiple benchmark datasets, showing that GSReuse significantly improves rendering, while maintaining high visual fidelity. Compared to state-of-the-art video frame reuse/generation methods, GSReuse delivers better image quality with much lower latency, facilitating practical deployment of 3DGS in VR applications.

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.

Abstract Rendering woven fabrics is challenging due to the complex micro geometry and anisotropy appearance. Conventional solutions either fully model every yarn/ply/fibre for high fidelity at a high computational cost, or ignore details, that produce non‐realistic close‐up renderings. In this paper, we introduce a model that shares the advantages of both. Our model requires only binary patterns as input yet offers all the necessary micro‐level details by adding the yarn/ply/fibre implicitly. Moreover, we design a double‐layer representation to handle light transmission accurately and use a constant timed () approach to accurately and efficiently depict parallax and shadowing‐masking effects in a tandem way. We compare our model with curve‐based and surface‐based, on different patterns, under different lighting and evaluate with photographs to ensure capturing the aforementioned realistic effects.

Abstract Digital replication of woven fabrics presents significant challenges across a variety of sectors, from online retail to entertainment industries. To address this, we introduce an inverse rendering pipeline designed to estimate pattern, geometry, and appearance parameters of woven fabrics given a single close‐up image as input. Our work is capable of simultaneously optimizing both discrete and continuous parameters without manual interventions. It outputs a wide array of parameters, encompassing discrete elements like weave patterns, ply and fiber number, using Simulated Annealing. It also recovers continuous parameters such as reflection and transmission components, aligning them with the target appearance through differentiable rendering. For irregularities caused by deformation and flyaways, we use 2D Gaussians to approximate them as a post‐processing step. Our work does not pursue perfect matching of all fine details, it targets an automatic and end‐to‐end reconstruction pipeline that is robust to slight camera rotations and room light conditions within an acceptable time (15 minutes on CPU), unlike previous works which are either expensive, require manual intervention, assume given pattern, geometry or appearance, or strictly control camera and light conditions.

Abstract We propose a new image‐based representation for real‐time distant objects rendering: Reshadable Impostors with Level‐of‐Detail (RiLoD). By storing compact geometric and material information captured from a few reference views, RiLoD enables reliable forward mapping to generate target views under dynamic lighting and edited material attributes. In addition, it supports seamless transitions across different levels of detail. To support reshading and LoD simultaneously while maintaining a minimal memory footprint and bandwidth requirement, our key design is a compact yet efficient representation that encodes and compresses the necessary material and geometric information in each reference view. To further improve the visual fidelity, we use a reliable forward mapping technique combined with a hole‐filling filtering strategy to ensure geometric completeness and shading consistency. We demonstrate the practicality of RiLoD by integrating it into a modern real‐time renderer. RiLoD delivers fast performance across a variety of test scenes, supports smooth transitions between levels of detail as the camera moves closer or farther, and avoids the typical artifacts of impostor techniques that result from neglecting the underlying geometry.

With the proliferation of mobile hardware-accelerated ray tracing, visual quality at low sampling rates (1spp) significantly deteriorates due to high-frequency noise and temporal artifacts introduced by Monte Carlo path tracing. Traditional spatiotemporal denoising methods, such as Spatiotemporal Variance-Guided Filtering (SVGF), effectively suppress noise by fusing multi-frame information and using geometry buffer (G-buffer) guided filters. However, their reliance on per-frame variance computation and global filtering imposes prohibitive overhead for mobile devices. This paper proposes an edge-aware, data-driven real-time denoising architecture within the SVGF framework, tailored explicitly for mobile computational constraints. Our method introduces two key innovations that eliminate variance estimation overhead: (1) an adaptive filtering kernel sizing mechanism, which dynamically adjusts filtering scope based on local complexity analysis of the G-buffer; and (2) a data-driven weight table construction strategy, converting traditional computational processes into efficient real-time lookup operations. These innovations significantly enhance processing efficiency while preserving edge accuracy. Experimental results on the Qualcomm Snapdragon 768G platform demonstrate that our method achieves 55 FPS with 1spp input. This frame rate is 67.42% higher than mobile-optimized SVGF, provides better visual quality , and reduces power consumption by 16.80% . Our solution offers a practical and efficient denoising framework suitable for real-time ray tracing in mobile gaming and AR/VR applications.

Abstract Normal map‐based microstructure rendering can generate both glint and scratch appearance accurately. However, the extra high‐resolution normal map that defines every microfacet normal may incur high storage and computation costs. We present an example‐based real‐time rendering method for arbitrary microstructure materials, which significantly reduces the required storage space. Our method takes a small‐size normal map sample as input. We implicitly synthesize a high‐resolution normal map from the normal map sample and construct MIP‐mapped 4D position‐normal Gaussian lobes. Based on the above MIP‐mapped 4D lobes and a LUT (lookup table) data structure for the synthesized high‐resolution normal map, an efficient Gaussian query method is presented to evaluate ‐NDFs (position‐normal distribution functions) for shading. We can render complex scenes with glint and scratch surfaces in real time ( 30 fps) with a full high‐definition resolution, and the space required for each microstructure material is decreased to 30 MB.

The interaction between light and materials is key to physically-based realistic rendering. However, it is also complex to analyze, especially when the materials contain a large number of details and thus exhibit “glinty” visual effects. Recent methods of producing glinty appearance are expected to be important in next-generation computer graphics. We provide here a comprehensive survey on recent glinty appearance rendering. We start with a definition of glinty appearance based on microfacet theory, and then summarize research works in terms of representation and practical rendering. We have implemented typical methods using our unified platform and compare them in terms of visual effects, rendering speed, and memory consumption. Finally, we briefly discuss limitations and future research directions. We hope our analysis, implementations, and comparisons will provide insight for readers hoping to choose suitable methods for applications, or carry out research.

Fur appearance rendering is crucial for the realism of computer generated imagery, but is also a challenge in computer graphics for many years. Much effort has been made to accurately simulate the multiple-scattered light transport among fur fibers, but the computation cost is still very high, since the number of fur fibers is usually extremely large. In this paper, we aim at reducing the number of fur fibers while preserving realistic fur appearance. We present an aggregated fur appearance model, using one thick cylinder to accurately describe the aggregated optical behavior of a bunch of fur fibers, including the multiple scattering of light among them. Then, to acquire the parameters of our aggregated model, we use a lightweight neural network to map individual fur fiber's optical properties to those in our aggregated model. Finally, we come up with a practical heuristic that guides the simplification process of fur dynamically at different bounces of the light, leading to a practical level-of-detail rendering scheme. Our method achieves nearly the same results as the ground truth, but performs 3.8×-13.5× faster.

Complex luminaires, such as grand chandeliers, can be extremely costly to render because the light-emitting sources are typically encased in complex refractive geometry, creating difficult light paths that require many samples to evaluate with Monte Carlo approaches. Previous work has attempted to speed up this process, but the methods are either inaccurate, require the storage of very large lightfields, and/or do not fit well into modern path-tracing frameworks. Inspired by the success of deep networks, which can model complex relationships robustly and be evaluated efficiently, we propose to use a machine learning framework to compress a complex luminaire's lightfield into an implicit neural representation. Our approach can easily plug into conventional renderers, as it works with the standard techniques of path tracing and multiple importance sampling (MIS). Our solution is to train three networks to perform the essential operations for evaluating the complex luminaire at a specific point and view direction, importance sampling a point on the luminaire given a shading location, and blending to determine the transparency of luminaire queries to properly composite them with other scene elements. We perform favorably relative to state-of-the-art approaches and render final images that are close to the high-sample-count reference with only a fraction of the computation and storage costs, with no need to store the original luminaire geometry and materials.

Abstract Microfacet theory is commonly used to build reflectance models for surfaces. While traditional microfacet‐based models assume that the distribution of a surface's microstructure is continuous, recent studies indicate that some surfaces with tiny, discrete and stochastic facets exhibit glittering visual effects, while some surfaces with structured features exhibit anisotropic specular reflection. Accordingly, this paper proposes an efficient and stationary method of surface material modeling to process both glittery and non‐glittery surfaces in a consistent way. Our method comprises two steps: in the preprocessing step, we take a fixed‐size sample normal map as input, then organize 4D microfacet trees in position and normal space for arbitrary‐sized surfaces; we also cluster microfacets into 4D K‐lobes via the adaptive k‐means method. In the rendering step, moreover, surface normals can be efficiently evaluated using pre‐clustered microfacets. Our method is able to efficiently render any structured, discrete and continuous micro‐surfaces using a precisely reconstructed surface NDF. Our method is both faster and uses less memory compared to the state‐of‐the‐art glittery surface modeling works.