Abstract Knitted fabrics present unique challenges for realistic rendering due to their complicated structure and scale‐dependent appearance. Existing methods typically rely on explicit yarn geometry, which is computationally complex, or texture‐based representations that require heavy storage and precomputed maps. In this paper, we introduce the first texture‐free, surface‐based appearance model for knitted fabrics, in which stitches are represented parametrically as thick curves and mapped directly onto fabric meshes. This avoids explicit yarn or fiber geometry, yet preserves the characteristic 3D look of yarn‐based models. Unlike prior surface‐based approaches, our method produces realistic volumetric effects such as depth, parallax, and silhouette preservation. To achieve this, we propose a curvature‐aware parallax mapping technique that ensures coherent appearance at grazing angles. Furthermore, we extend the appearance model to a multi‐scale formulation that aggregates geometry and visibility over texture footprints and adjusts roughness parameters for stable far‐field rendering. Our model combines the efficiency and simplicity of surface‐based methods with the volumetric realism of fiber‐based models, reproducing characteristic knit effects such as 3D stitch structure in a multi‐scale manner without the complexity or storage cost of texture‐based approaches.

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.

Colorization involves the synthesis of colors while preserving structural content as well as the semantics of the target image. This problem has been well studied for 2D photographs with many state-of-the-art solutions. We explore a new challenge in the field of colorization where we aim at colorizing multi-modal 3D medical data using 2D style exemplars. To the best of our knowledge, this work is the first of its kind and poses challenges related to the modality (medical MRI) and dimensionality (3D volumetric images) of the data. Our approach to colorization is motivated by modality conversion that highlights its robustness in handling multi-modal data.

Abstract A good transfer function in volume rendering requires careful consideration of the materials present in a volume. A manual creation is tedious and prone to errors. Furthermore, the user interaction to design a higher dimensional transfer function gets complicated. In this work, we present a graph‐based approach to design a transfer function that takes volumetric structures into account. Our novel contribution is in proposing an algorithm for robust deduction of a material graph from a set of disconnected edges. We incorporate stable graph creation under varying noise levels in the volume. We show that the deduced material graph can be used to automatically create a transfer function using the occlusion spectrum of the input volume. Since we compute material topology of the objects, an enhanced rendering is possible with our method. This also allows us to selectively render objects and depict adjacent materials in a volume. Our method considerably reduces manual effort required in designing a transfer function and provides an easy interface for interaction with the volume.