This is an erratum for the article “Guidestar-Free Adaptive Optics with Asymmetric Apertures” published in ACM Trans. Graph. 45, 5, Article 39 (June 2026), 17 pages.
This is an erratum for the article “Robust and Efficient Penetration-Free Elastodynamics without Barriers” published in ACM Trans. Graph. 45, 5, Article 40 (June 2026), 20 pages.
This is an erratum for the article “Thermal Non-Line-of-Sight Imaging through Rough Surfaces” published in ACM Trans. Graph. 45, 5, Article 41 (June 2026), 21 pages.
The advancements in low-cost manufacturing hardware have enabled inexperienced users to create 3D product prototypes in-house. As a common issue, however, the resulting 3D models, despite their aesthetic appeal, often lack the structural integrity required for practical use. While there has been significant research into automated shape adjustment techniques, these methods rely on additional inputs such as parametric representations and specific external load conditions, making them inaccessible to novice users. To address this challenge, we propose a shape optimization technique that does not require extra inputs. Starting from a target shape represented by a general 3D mesh, our method aims to produce an optimized shape that is both visually consistent and structurally more robust. Our approach formulates the problem as an approximate bilevel optimization: the inner problem solves a compliance-based surrogate to identify a critical load direction, while the outer problem minimizes the maximum stress across the entire shape surface. We develop an approximate bilevel solver that alternates between solving the outer and inner problems, as well as regular mesh refinement. Our empirical results demonstrate the effectiveness of our method in finding 3D shapes that are visually consistent, while also providing the necessary robustness for practical applications.
Gauge equivariant networks are emerging for equivariant representation learning on manifolds. However, existing gauge equivariant networks suffer from the dilemma between restricted SO(2) irreducible representations with equivariance guarantee to arbitrary continuous gauge transformations and enhanced C N regular representations with limited equivariance to finite discretized gauge transformations. In this paper, we propose a gauge equivariant capsule network (GECN) for manifolds that guarantees mathematically provable gauge equivariance in SO(2) without sacrificing the expressive capability. We develop a novel gauge equivariant capsule (GEC) that leverages the spatio-capsule dynamic routing algorithm to jointly exploit the capsule-wise and spatial dependencies in a local region of manifolds and achieve gauge equivariant representation based on real irreducible representations of SO(2). Furthermore, we propose a gauge equivariant capsule network (GECN) for manifolds by designing initial pose extractor, GEC-based residual block, and gauge equivariant pooling and unpooling based on GECs. We show that GECN is general to unify existing gauge equivariant networks and demonstrate in theory that it is equivariant to continuous gauge transformations in SO(2). Experimental results demonstrate that GECN achieves state-of-the-art performance in the tasks of deformable shape classification and segmentation and classification of spherical image and 3-D objects without principal curvature direction.
Cloud rendering has gained much attention recently since it alleviates the client’s computing cost by distributing most of the rendering tasks on the cloud, and the rendered frames are compressed and streamed to a client device for display. However, the accessibility of cloud rendering is still limited by the network communication cost to stream high-resolution frames. By introducing neural networks into the video compression algorithms, SOTA methods have achieved a better compression ratio than existing video codecs like H.265. However, none of them are designed specifically for rendered content, in which features like motion vectors, albedo, and normal are readily available in nowadays rendering pipelines. We propose a new end-cloud collaborative rendering and streaming framework that generates these auxiliary features on both the end and cloud sides as a simultaneous prior, and utilizes this prior to guide both the encoding and decoding of frame compression. Our framework can empower low-end devices with high-end visual effects and reduce the cost of establishing high-speed network connections. Results show that our streaming pipeline is highly efficient and achieves higher compression ratio compared with standard video codecs and other neural compression methods.
Multi-axis fused filament fabrication (FFF) reduces staircase artifacts and support material by adapting nozzle orientation. However, reliable and highly customizable printing requires layers and toolpaths combining a number of challenging properties: following prescribed fields, preserving constant bead geometry, avoiding stops, retractions, and collisions. We present AtomSlicer, which takes a 3D tool-orientation field and perlayer 2-RoSy tangent direction fields, to generate field-aligned non-planar layers of near-constant thickness. It then computes collision-free deposition toolpaths that are continuous within each layer, and as continuous as possible across layers. AtomSlicer encodes geometry with three orthogonal phase fields sampled into oriented atoms, partitions them into fabricable layers, reconstructs layer meshes, and synthesizes 2-RoSy-aligned toolpaths. Unlike prior methods it treats user- or optimizer-defined fields as constraints, producing a feasible toolpath or reporting non-fabricability rather than modifying the specification. We validate on varied shapes, including a large Thingi10k study, and 13 printed models using constant extrusion per unit path length under a fixed bead profile. AtomSlicer reduces non-extruding travel to a few percent, cuts travel moves by one to two orders of magnitude versus planar and Atomizer baselines, and is 9×-60× faster than Atomizer.
This is an erratum for the article “Loops2Roofs: Diffusion-based 3D Roof Generation using a Loop Representation” published in ACM Trans. Graph. 45, 5, Article 38 (June 2026), 16 pages.
Our goal in this article is to propose a fast and easy to implement BRDF modeling method that provides both accurate and compact representations for all types of BRDF, i.e., isotropic or anisotropic. To achieve this objective, we use a Bayesian regression method with a Gaussian process prior which allows obtaining compact BRDF representations in a purely analytical way. For this purpose, we use a generalzed distance covariance kernel which is much better suited to BRDF features than the usual Gaussian kernel. To speed up the processing, we adapt this method to the specificities of BRDFs through an appropriate input data structure and distribution of observations so as to drastically reduce the problem dimensionality through an efficient factorization method. In this way, all calculations at both fitting and rendering steps are reduced to basic matrix products and the computation of a BRDF representation with our modeling method takes only a few seconds. Furthermore, rather than using a systematic approach as in state-of-the-art methods, the size and complexity of the BRDF representation can be adapted to the application requirements as regards the fitting accuracy and rendering constraints. Besides, our BRDF representation can be easily converted to spherical harmonics expansions, which allows easier integration in usual rendering algorithms. We also propose importance sampling methods derived from our BRDF modeling method that leads to fast and easy implementations. Experimental applications of our method to various types of isotropic and anisotropic BRDFs show that state-of-the-art methods can be outperformed in most cases by using a small set of observations for the regression.
Diffusion models and auto-regressive models have been widely adopted in recent research on 3D shape generation. However, diffusion models and standard next-token prediction auto-regressive models require dozens to even hundreds of generation steps, leading to slow inference. A new paradigm of auto-regressive with next-scale prediction has demonstrated both inference efficiency and generation quality. However, existing works are restricted to fixed-length representations and cannot be directly applied to more adaptive variable-length representations, such as sparse voxels. To address these limitations, we propose Grow3D , a new auto-regressive generative framework that generates high-quality 3D shapes in a coarse-to-fine manner via ”next-scale prediction”, achieving both high quality and fast inference. Specifically, we first employ a Vector Quantized Variational Autoencoder (VQ-VAE) with residual serialization to encode 3D shapes into a multiscale, sparse-structured latent representation with quantized features. Building on this latent representation, we utilize a next-scale prediction strategy to auto-regressively generate both the octree structure and the corresponding geometry features. Benefiting from the inherent structure of the octree, we introduce an octree-structure-aware attention mechanism that selectively attends to the most relevant features. Furthermore, a CFG-based sampling strategy is proposed to enhance the quality and diversity of generation. Extensive experiments demonstrate that Grow3D outperforms state-of-the-art methods in both 3D shape generation quality and speed, enabling some real-time downstream applications, such as interactive 3D editing.
We introduce a new offset meshing method that handles clean 3D surface meshes of arbitrary geometry and topology—where “clean” refers to meshes that are watertight, manifold, and free of self-intersections. Our approach also extends to imperfect, or “dirty,” meshes that violate these conditions, although the problem becomes significantly more difficult in such scenarios, and faithful feature preservation near defective areas cannot always be assured. In contrast to prior techniques, which have largely focused on constant-radius offsets, our method is, to our knowledge, the first to support mitered offsets while effectively preserving sharp features. Our method is designed based on several core principles: (1) explicitly generating the offset vertices and triangles with feature-capturing energy and constraints; (2) prioritizing the generation of the offset geometry before establishing its connectivity, (3) employing exact algorithms in critical pipeline steps for robustness, balancing the use of floating-point computations for efficiency, (4) applying various conservative speed up strategies including early reject non-contributing computations to the final output. Our approach further uniquely supports variable offset distances on input surface elements, offering a wider range of practical applications compared to conventional methods. For benchmarking purposes, we performed an extensive comparison against state-of-the-art offset methods using a curated subset of the Thingi10K dataset. Our results demonstrate the superiority of our approach over current state-of-the-art methods in terms of element count, feature preservation, and non-uniform offset distances of the resulting offset mesh surfaces, marking a significant advancement in the field.
3D artists blend geometry and texture to craft objects and their appearance, using both geometric and image processing tools. Yet, it is difficult for geometry processing to account for textures in the standard UV with texture maps representation. When textures are represented as vertex colors though, we show texture-aware geometry processing becomes straightforward. Unfortunately, most textured meshes are represented with UV parameterizations, so in practice most geometry processing tools simply ignore texture. This scarcity of vertex colors meshes is remedied using a new remeshing approach that hoists textures to vertex colors through per-triangle remeshing, while preserving input appearance. This new remeshing takes meshes with UVs and texture maps and generates vertex color meshes, which are then used to show how vertex colors can be incorporated into geometry processing through texture-aware modifications to multiple applications. Specifically, we test texture-aware geometry processing on surface segmentation, Tutte parameterizations, surface texture processing algorithms such as edge-detection, and vector field operations with the texture gradient. For each application, texture information produces different and in some sense improved results as compared to geometry-only algorithms, or image processing algorithms performed in UV space.
Data-driven methods leveraging deep reinforcement learning have become the dominant paradigm for developing controllers that enable physically simulated characters to produce natural human-like behaviors. However, these data-driven methods often struggle to adapt to novel environments and compose diverse skills to perform more complex interaction tasks with the environment. To address these challenges, we propose a hybrid imitation learning (HIL) framework that combines motion tracking, for precise skill replication, with adversarial imitation learning, to enhance adaptability and skill composition, enabling robust dynamic control for highly athletic behaviors. This hybrid learning framework is implemented through parallel multi-task environments and a unified observation space, utilizing a goal-conditioned representation to facilitate knowledge-sharing across the hybrid parallel environments. We demonstrate the effectiveness of HIL on a parkour-style obstacle traversal task and a heading control task. Our framework enables a unified controller that not only preserves the naturalness of reference motion data, but also generalizes effectively to challenging new environments. Evaluations across procedurally generated tasks and baselines show that our method improves motion quality, increases skill diversity, and achieves competitive task completion compared to previous learning-based approaches. Results are best visualized through https://jiashunwang.github.io/HIL.
A common process in authoring digital scenes for games, films, and virtual environments is for artists to construct 3D geometry that matches a 2D perspective reference image. In the case of the bare-earth terrain, this is typically a manual process since, unlike for trees and buildings, few inverse reconstruction methods currently exist. To address this, we introduce a method for automatically inferring a detailed, consistent, and complete terrain heightmap from a single photographic image. Our initial phase involves extracting camera parameters and a 3D pointmap from the input image, which is then transformed into a heightmap. However, this only recovers the unoccluded portions of the terrain visible from the perspective of the image. The next phase thus entails the generation of plausible occluded regions using a diffusion model trained on terrain elevation data. The entire process is guided by three consistency principles: geomorphological consistency (the features of the occluded terrain resemble the visible portions), hydrological consistency (the river network is uninterrupted and flows reliably), and view consistency (the shape of the rendered terrain accurately matches the input image). We demonstrate that our method obeys these principles, reliably generates terrains across various scales, and integrates with scene authoring workflows.
Optically recorded analog holograms can reconstruct photorealistic three-dimensional (3D) images without the need for specialized eyewear. Computer-generated holograms (CGHs) are created by simulating the holographic recording process digitally rather than capturing them optically. Large-scale 3D still-image reconstruction with wide-viewing-zone can be achieved by mapping the amplitude or phase profiles of CGHs onto diffractive optical elements (DOEs), which modulate in-plane wavefront distribution using wavelength-scale surface structures. However, DOEs exhibit limited wavelength selectivity, interacting with a broad spectral range beyond their design wavelength. This results in reduced overall transmittance and typically requires three separate CGHs for full-color reconstruction. In this work, we present an "invisible holographic window," a transparent, surface-relief CGH patterned directly on glass via laser grayscale lithography. By encoding the real component of the interference pattern and reducing the phase-modulation range, the surface-relief structure transitions from deeply wrapped, jagged phase profiles to shallower and smoother sinusoidal phase profiles, which is associated with improved optical transmittance. Furthermore, a spatial-frequency-domain band-division multiplexing strategy is applied to support crosstalk-free, full-color 3D image reconstruction from a single transparent CGH, albeit with a viewing angle reduction. This platform generates photorealistic full-color 3D still-images in real space, offering new possibilities for transparent augmented-reality display interfaces such as those integrated into storefront windows, office glass partitions, and museum display cases. Our findings pave the way for advanced holographic display technologies and promise to accelerate research in the field.
The household rearrangement task involves spotting misplaced objects in a scene and accommodate them with proper places. It depends both on common-sense knowledge on the objective side and human user preference on the subjective side. In achieving such a task, we propose to mine object functionality with user preference alignment directly from the scene itself, without relying on human intervention. To do so, we work with scene graph representation and propose LLM-enhanced scene graph learning which transforms the input scene graph into an Affordance Enhanced Graph (AEG) with information-enriched nodes and newly discovered edges (relations). In AEG, the nodes corresponding to the receptacle objects are augmented with context-induced affordance which encodes what kind of carriable objects can be placed on it. New edges are discovered with newly discovered non-local relations. With AEG, we perform task planning for scene rearrangement by detecting misplaced carriables and determining a proper placement for each of them. We implement an end-to-end robot system for autonomous household rearrangement in unseen environments and test our method by implementing a tiding robot in both simulated environments and real-world scenarios, and perform evaluation on a new benchmark we build. Extensive evaluations demonstrate that our method achieves state-of-the-art performance in misplacement detection and rearrangement planning.
This is an erratum for the article “Generalized Aberrations for Processing-Aware Optical Design” published in ACM Trans. Graph. 45, 5, Article 44 (June 2026), 17 pages.
This is an erratum for the article “A Robust and Efficient Intersection Algorithm for NURBS Surfaces: Handling Small Loops and Tangent Intersections” published in ACM Trans. Graph. 45, 5, Article 37 (June 2026), 19 pages.
This is an erratum for the article “Tensor Decomposition-Based Four-Dimensional Background-Oriented Schlieren Tomography for High-Speed, High-Fidelity Flow Field Reconstruction” published in ACM Trans. Graph. 45, 5, Article 35 (June 2026), 19 pages.
This is an erratum for the article “OmniHands: Robust Motion Capture of Interactive Hands via A Versatile Transformer” published in ACM Trans. Graph. 45, 5, Article 34 (June 2026), 17 pages.
