We present a method for automatically converting strand-based hair models into an efficient mesh-based representation, known as hair cards, for real-time rendering. Our method takes strands as inputs and outputs polygon strips with semi-transparent texture, preserving the appearance of the original strand-based hairstyle. To achieve this, we first cluster strands into groups, referred to as wisps, and generate hairstyle-preserving texture maps for each wisp by skinning-based alignment of the strands into a normalized pose in UV space. These textures can further be shared among similar wisps to better utilize the limited texture resolution. Next, polygon strips are fitted to the clustered strands via tailored differentiable rendering that can optimize transparent cluster-colored coverage masks. The proposed method successfully handles a wide range of hair models and outperforms existing approaches in representing volumetric hairstyles such as curly and wavy ones. Furthermore, our strip optimization can efficiently convert a full-hair model with more than 100 thousand strands within 20 seconds. Our method was extensively tested on both a hair database and many complex real-world hairstyles acquired using state-of-the-art hair capture methods.

Abstract Gradient‐based optimization is a fundamental tool in geometry processing, but it is often hampered by geometric distortion arising from noisy or sparse gradients. Existing methods mitigate these issues by filtering (i.e., diffusing) gradients over a surface mesh, but they require explicit mesh connectivity and solving large linear systems, making them unsuitable for point‐based representation. In this work, we introduce a gradient filtering method tailored for point‐based geometry. Our method bypasses explicit connectivity by leveraging regularized Green's functions to directly compute the filtered gradient field from discrete spatial points. Additionally, our approach incorporates elastic deformation based on Green's function of linear elasticity (known as Kelvinlets), reproducing various elastic behaviors such as smoothness and volume preservation while improving robustness in affine transformations. We further accelerate computation using a hierarchical Barnes–Hut style approximation, enabling scalable optimization of one million points. Our method significantly improves convergence across a wide range of applications, including reconstruction, editing, stylization, and simplified optimization experiments with Gaussian splatting.

We present the Mokume dataset for solid wood texturing consisting of 190 cube-shaped samples of various hard and softwood species documented by high-resolution exterior photographs, annual ring annotations, and volumetric computed tomography (CT) scans. A subset of samples further includes photographs along slanted cuts through the cube for validation purposes. Using this dataset, we propose a three-stage inverse modeling pipeline to infer solid wood textures using only exterior photographs. Our method begins by evaluating a neural model to localize year rings on the cube face photographs. We then extend these exterior 2D observations into a globally consistent 3D representation by optimizing a procedural growth field using a novel iso-contour loss. Finally, we synthesize a detailed volumetric color texture from the growth field. For this last step, we propose two methods with different efficiency and quality characteristics: a fast inverse procedural texture method, and a neural cellular automaton (NCA). We demonstrate the synergy between the Mokume dataset and the proposed algorithms through comprehensive comparisons with unseen captured data. We also present experiments demonstrating the efficiency of our pipeline's components against ablations and baselines. Our code, the dataset, and reconstructions are available via https://mokumeproject.github.io/.

Abstract Volumetric radiance fields have recently gained significant attention as promising representations of photorealistic scene reconstruction. However, the non‐photorealistic rendering of such a representation has barely been explored. In this study, we investigate the artistic posterization of the volumetric radiance fields. We extend the recent palette‐based image‐editing framework, which naturally introduces intuitive color manipulation of the posterized results, into the radiance field. Our major challenge is applying stylization effects coherently across different views. Based on the observation that computing a palette frame‐by‐frame can produce flickering, we propose pre‐computing a single palette from the volumetric radiance field covering its entire visible color. We present a method based on volumetric visibility to sample visible colors from the radiance field while avoiding occluded and noisy regions. We demonstrate our workflow by applying it to pre‐trained volumetric radiance fields with various stylization effects. We also show that our approach can produce more coherent and robust stylization effects than baseline methods that compute a palette on each rendered view.

We developed a direction system for assisting the work in a real world from a distant site. At first, the 3-D shape of the object is measured and sent to the distant PC. A supervisor at the distant site can observe the CG of the object and draw annotation figures on it. The figures of the direction message are projected onto the object using projectors. The worker is free from any wearing equipment, ex. HMD, and multi projectors avoid the problem of occlusion by the worker body.