SEARCH · Search arXiv

Results for “cs.GR”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

7,800 records · Page 2Linked to original sources

arXiv2026

Telligram: Text-Driven Calligram Generation via Diffusion-Guided Skeleton Optimization

Compact calligram generation aims to form a semantic shape while keeping letters recognizable. Most existing methods are shape-conditioned and mainly solve downstream letter layout inside a given contour. We study text-only calligram generation without an input contour. This setting is difficult because semantic shape formation and letter readability strongly interfere with each other when optimized in a single stage. Pushing the word toward a clear figure can easily damage glyph structure, while preserving readable letters can weaken the target shape. To address this difficulty, we present Telligram, a training-free, low-tuning, two-stage framework composed of Semantic Occupancy Prior Formation and Readability-Constrained Glyph Realization. The first stage uses Variational Score Distillation (VSD) with structured skeleton optimization and hierarchical gradient projection to produce a semantic occupancy prior. The second stage converts this occupancy prior into per-letter regions and reconstructs readable glyph layouts through lightweight geometric processing. The framework generates coherent and creative word-level semantic calligrams directly from text prompts.

cs.GR↗

arXiv2026

Cycle Counting and Character Expectations Using Alternating Structures

Recently, two related papers [arXiv:2412.13941, arXiv:2409.03626] found a connection between two subjects: the w-cycle theorem, which is a theorem about counting appearances of cycles reading out a word w in certain graphs, and character expectations on word measures. The w-cycle theorem was proven independently by [arXiv:1410.2540] using stackings and by [arXiv:1410.2579] using bislim structures. In the current work, we generalize stackings and bislim structures to alternating stackings and alternating bislim structures. We show how this significantly strengthens the w-cycle theorem for words admitting such alternating structures, and as a result, also strengthens the recent results of [arXiv:2412.13941] and [arXiv:2409.03626]. We show that generic words admit alternating bislim structures, and therefore, the strengthened results hold for generic words. Using our new machinery, we address conjectures of Wilton, of Hanany-Puder and of Puder-Shomroni. We prove that all three conjectures hold for generic words, but we also find counterexamples for the first two.

math.GR↗

arXiv2026

TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields

We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields. Our key insight is to represent mesh topology as a nearest-vertex vector field (NVF) defined over the surface, where each point encodes its association to the nearest triangle vertex in the local barycentric frame. We train a latent flow-matching model to synthesize this field, enabling topology generation conditioned on the input geometry. To extract a coherent mesh, we cluster surface regions using the generated NVF and guide a constrained quadric error metric (QEM) mesh simplification with topology-aware optimization. This yields output meshes that closely match the input geometry while exhibiting structured, artist-like connectivity. Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches, alongside 90% lower Chamfer Distance and an 8x speedup.

cs.CV↗

arXiv2026

RefRetouch: Personalized Image Retouching without Test-time Fine-tuning

Personalized image retouching aims to adapt retouching styles of individual users from reference examples, but existing methods often require user-specific fine-tuning or fail to generalize effectively. To address these challenges, we introduce \textbf{RefRetouch}, a general framework for personalized image retouching that instantly adapts to user retouching styles without any test-time fine-tuning. It employs an \textit{asymmetric auto-encoder} to encode the retouching style from paired examples into a content disentangled latent representation that enables faithful transfer of the retouching style to new images. To adaptively apply the encoded retouching style to new images, we further propose \textit{retrieval-augmented retouching} (RAR), which retrieves and aggregates style latents from reference pairs most similar in content to the query image. With these components, \textbf{RefRetouch} enables superior and generic content-aware retouching personalization across diverse scenarios, including single-reference, multi-reference, and mixed-style settings, while also generalizing out of the box to photorealistic style transfer.

cs.GR↗

arXiv2026

TruncGradGS: Improved 3D Gaussian Splatting via Truncated Gradient Updates

3D Gaussian Splatting has become a de facto scene representation for novel view synthesis, yet robustly learning 3D Gaussian primitives from visual input remains challenging. Standard optimization relies on gradient-based updates, but a common issue is the gradient vanishing phenomenon: a pixel far from a Gaussian primitive often has diminishing gradient magnitudes to influence primitive attributes, resulting in suboptimal scene reconstruction. In this paper, we propose a method to address gradient vanishing with a piecewise truncated gradient formulation that improves the optimization stability and robustness to initializations. We show that our method consistently improves 3D Gaussian Splatting with random and COLMAP initializations while being generalizable across static and dynamic Gaussian Splatting. As a by-product, we also examine the limitations of current benchmarks for dynamic scenes, and introduce a novel dataset for benchmarking dynamic Gaussian Splatting using synthetic 3D scenes. We demonstrate the effectiveness of our method in both static and dynamic settings for the public benchmarks and our proposed dataset.

cs.CV↗

arXiv2026

DReSG: Diffusion Residuals for Stylized Gaussian Splatting

Reference-guided stylization of scenes represented by 3D Gaussian Splatting (3DGS) is important for efficient and controllable 3D content creation. Existing VGG-feature-based 3D stylization methods provide stable rendered-view optimization, but often under-represent expressive reference style cues; diffusion models offer stronger image priors, yet direct per-view or score-based diffusion guidance can lead to view drift, local artifacts, and hard-to-control appearance updates. We present DReSG, a 3D-grounded residual-feedback framework for stylized Gaussian splatting. DReSG represents attention-guided diffusion proposals as residual targets relative to the current render, and progressively absorbs these residuals into a shared Gaussian scene through multi-view Gaussian feedback. To make this feedback stable and controllable, DReSG modulates residual strength during target construction and combines coverage-aware view selection with conflict-filtered color updates during multi-view fitting. Extensive experiments demonstrate that DReSG achieves competitive reference-guided stylization while better preserving scene structure and cross-view stability. Our project page is available at https://vpx-ecnu.github.io/DReSG-website/.

cs.CV↗

arXiv2026

No Pixel Left Behind: Filling Gaps in Anime Colorization

Animation production workflows often involve digital colorization of line art, where small unpainted regions ("gaps") frequently occur and remain an underexplored challenge. We conducted a formative study in Japanese animation (anime) pipelines and found that while the paint bucket tool is widely used for base coloring, tiny enclosed areas are frequently overlooked, resulting in time-consuming manual detection and filling. We introduce GapFill, a tool grounded in professional practices that reduces the effort of gap detection, zooming, and color selection. Our deep-learning method suggests appropriate fill colors by referencing surrounding regions, leveraging the flat-color nature of anime-style images. In a user study with 13 professional colorists, our system improved performance and usability in gap-filling tasks over conventional methods. The study also suggested that prediction accuracy alone is not the primary factor for usability, that appropriate colors can be contextually ambiguous, and that GapFill can complement existing tools depending on users' trust in new AI-powered assistance.

cs.HC↗

arXiv2026

TetraSDF: Analytic Isosurface Extraction with Multi-resolution Tetrahedral Grid

Extracting an explicit surface that exactly matches the zero-level set of a neural signed distance function (SDF) remains challenging. Sampling-based isosurfacing methods such as Marching Cubes introduce discretization error. In contrast, continuous piecewise affine (CPWA) analytic approaches typically require plain ReLU MLPs, which limits the ability to learn high-frequency SDFs in practice. We present TetraSDF, an analytic isosurface extraction framework for SDFs that retains the expressiveness of grid-based encoders while enabling exact zero-level set extraction, by representing the SDF with a ReLU MLP composed with a multi-resolution tetrahedral positional encoder. Our positional encoder's barycentric interpolation preserves a global CPWA structure, allowing us to track ReLU linear regions within an encoder-induced polyhedral complex. We further introduce a fixed analytic input preconditioner derived from the encoder's metric to reduce directional bias, thereby stabilizing training. Across multiple benchmarks, TetraSDF matches or surpasses existing grid-based encoders in SDF reconstruction accuracy, while faithfully recovering the network's zero-level set as a triangle mesh.

cs.CV↗

arXiv2026

STyMo: Fast and Controllable Few-Shot Motion Style Transfer

Supporting a wide variety of motion styles is critical for creating diverse virtual characters, but current methods either require large stylized datasets or pre-trained models that cannot generalize beyond their training distribution. We present STyMo, a few-shot approach that learns motion style from only seconds of paired data and trains in one to two minutes. Our key insight is to decompose style into two components: a static component capturing time-invariant posture, and a temporal component capturing frame-wise dynamics. This decomposition yields an interpretable system where posture intensity, temporal exaggeration, and per-body-region style can be adjusted at runtime. Furthermore, the reduction in required training data and computation time structurally permits an iterative authoring workflow. To ensure robustness on arbitrary inputs, we further introduce a stylizability gate that automatically prevents artifacts on out-of-distribution motions. We demonstrate results across diverse motion styles, from subtle emotional variations to exaggerated character archetypes, and release our processed paired dataset to facilitate future research.

cs.GR↗

arXiv2026

Projective Affine Body Dynamics for Multibody Systems

Multibody systems have widespread applications in diverse fields such as robotics, entertainment, and animation. Their capability to model and simulate intricate interactions among interconnected bodies makes them invaluable in various domains. However, complexity arises with factors like non-smooth contact handling, nonlinearity in flexible joints, and parallelization challenges. We introduce a stable and highly parallel GPU algorithm within affine body dynamics for solving constrained multibody dynamics with nonlinear constraints.Our innovation involves reformulating constrained multibody dynamics into a variational form, treating the system as a set of affine bodies connected with peridynamic bonds. This formulation provides a unified model for affine body dynamics, constraints, and contact within the peridynamics framework.It also facilitates the integration of the semi-implicit successive substitution method to solve nonlinear optimization in a global-local iterative manner. The proposed method obviates the necessity of assembling a global Hessian, rendering it highly suitable for efficient implementation on GPUs.This allows real-time simulation of intricate interactions involving both rigid and flexible bodies, encompassing contact, joint constraints, and friction.

cs.GR↗

arXiv2024

A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions

We introduce a neural-preconditioned iterative solver for Poisson equations with mixed boundary conditions. Typical Poisson discretizations yield large, ill-conditioned linear systems. Iterative solvers can be effective for these problems, but only when equipped with powerful preconditioners. Unfortunately, effective preconditioners like multigrid require costly setup phases that must be re-executed every time domain shapes or boundary conditions change, forming a severe bottleneck for problems with evolving boundaries. In contrast, we present a neural preconditioner trained to efficiently approximate the inverse of the discrete Laplacian in the presence of such changes. Our approach generalizes to domain shapes, boundary conditions, and grid sizes outside the training set. The key to our preconditioner's success is a novel, lightweight neural network architecture featuring spatially varying convolution kernels and supporting fast inference. We demonstrate that our solver outperforms state-of-the-art methods like algebraic multigrid as well as recently proposed neural preconditioners on challenging test cases arising from incompressible fluid simulations.

math.NA↗

arXiv2026

ExMesh: Explicit Mesh Reconstruction with Topology Adaptation

Reconstructing surface meshes from multi-view images has remained a core challenge in recent years. Most existing methods, whether implicit or explicit, depend on intermediate representations and post-processing steps like Marching Cubes or TSDF fusion, often resulting in artifacts and fragmented geometry. Directly optimizing explicit meshes is a promising approach. However, it presents two critical challenges. The first is how to adaptively refine mesh topology to capture detail without introducing degenerate faces. The second is how to maintain consistent UV coordinates for high-fidelity texturing as the mesh structure evolves. To overcome these, we propose ExMesh, a novel framework that directly optimizes explicit meshes by integrating differentiable optimization with discrete topology updates. Specifically, we introduce an adaptive vertex splitting and merging strategy, along with real-time UV maintenance, to enable coarse-to-fine optimization while preserving geometric integrity. To our knowledge, ExMesh is the first framework to seamlessly integrate discrete topology operations into a continuous differentiable optimization pipeline. Extensive experiments demonstrate that ExMesh achieves a balance among accuracy, computational efficiency, and mesh conciseness.

cs.CV↗

arXiv2026

Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges

This paper surveys the state of transparency rendering in Computer-Aided Design (CAD), with a focus on both practical deployment in industrial systems and the underlying algorithms. We first review current approaches to transparency rendering in CAD environments and outline application scenarios in which accurate and performant transparency is critical for design inspection, communication, and decision-making. We then analyze the trade-offs between approximate and exact transparency techniques, comparing their performance-quality balance on desktop and mobile platforms and discussing criteria for selecting appropriate methods. The survey further identifies the need for robust benchmarks, quality metrics, and evaluation methodologies tailored to CAD-specific visualization tasks. We examine techniques for emphasizing important interior components such as importance-driven transparency, silhouette-based methods, and related approaches to support effective spatial understanding in complex assemblies. Finally, we discuss the unique challenges of rendering transparent constructive solid geometry (CSG) objects, including robustness, correctness, and integration with modern rendering pipelines. Collectively, these contributions characterize current capabilities, systematize open problems, and outline future research directions for transparency rendering in CAD visualization.

cs.GR↗

arXiv2026

ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction

We propose ECHO for dyadic 3D facial motion generation under a strict dual-stream audio-only setting, formulating the problem as an asymmetric task involving speech-constrained articulation and one-to-many listener reactions. To address this asymmetry, ECHO decomposes motion into a deterministic anchor that captures stable speech-correlated structure and a stochastic residual that models the remaining one-to-many interaction dynamics. On top of this backbone, Motion Memory acts as a training-only regularizer during brief late-stage fine-tuning to provide local priors for weakly conditioned listening windows, while semantic-group scaling controls residual injection across expression, jaw, and neck. This design balances speaking-side articulatory fidelity with listening-side realism and diversity in a single generation process. Results from unified, state-wise, and ablation evaluations show that conversational 3D motion benefits from decomposing stable and uncertain components rather than applying stochasticity uniformly. ECHO provides a practical formulation and technical basis for deployable conversational digital humans under strict audio-only conditions.

cs.GR↗

arXiv2026

Inverse Rig Optimization from Line Drawings

Stylized 3D character animation is largely hand-authored, with animators authoring rig parameters one keyframe at a time to find the best pose. Because stylized work reads chiefly through contour lines, drawing contours in the camera view is the most direct and precise way to express artistic intent. This mismatch between the rig controls and the artist's goal forces a laborious trial-and-error workflow, with animators repeatedly manipulating rig controls against the rendered view to match the desired contour. To address this, we propose a method that recovers rig parameters from screen-space contour strokes, enabling effective keyframing from sketches. Given strokes that redraw the current contour, our method optimizes the high-level rig parameters defined in the DCC tool. The key is to use a pre-trained MLP rig surrogate that provides a differentiable map from rig parameters to mesh vertices, replacing the original black-box rig within the optimization process. We match user-drawn lines to mesh contour lines and backpropagate the resulting screen-space error through the surrogate to update the rig parameters. Our results demonstrate that the method works for diverse characters and practical scenarios.

cs.GR↗

arXiv2026

RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives

We present 'RenderFormer-V2', a unified learned transformer-based neural rendering model, complementary to modern physics-based rendering systems, that can handle diverse light-transport effects such as caustics, volumetric scattering, environment lighting, textured and displaced surfaces and out-of-distribution materials without per-scene training or specialized code. RenderFormer-V2 models global light transport as a sequence-to-sequence transformation. Following its predecessor, RenderFormer-V2 also employs a two stage process: a view-independent stage that resolves intra-scene primitive to primitive transport, and a view-dependent stage that transforms the internal neural scene representation into image pixels. Different from RenderFormer, our model employs a novel combined windowed-attention and rendering-informed attention sink in the view-independent stage to improve scalability while maintaining render accuracy. To further improve versatility, RenderFormerV2 supports heterogeneous scene primitives, including environment maps and participating media, and it employs a material encoding independent of the underlying surface reflectance model that encodes material appearance via a novel neural embedding. We demonstrate the versatility of RenderFormer-V2 on a variety of scenes and perform an extensive ablation of the improved attention mechanism.

cs.CV↗

arXiv2026

TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking

Experience-driven manufacturing, such as garment pattern making, faces a severe generational skills gap because its core expertise relies on undocumented tacit knowledge forged through day-to-day practice. To address this challenge, we present TailorCoPilot, an agentic pattern-making system built upon a specially designed version-control backend TailorTrace. TailorTrace models sewing patterns as structured, discrete states and records their transformations during the pattern-making process as explicit operation sequences defined upon the geometry primitives in the sewing pattern (panels, edges, vertices and stitches). Integrated into a conventional pattern-making GUI, TailorTrace enables seamless documentation of senior experts' tacit pattern-making knowledge without breaking their daily workflow. The documented knowledge further offers interactive, pedagogical scaffolding for novices, while providing a robust foundation to power TailorCoPilot and train future generative AI models. In a user study with novices and advanced novices, TailorCoPilot improved task completion rates, reduced time and perceived workload, and yielded higher-quality artifacts compared to skill-appropriate baselines. Ultimately, TailorCoPilot demonstrates a viable pathway to capture practice-based expertise, operationalizing it to support both generative AI advancements and human apprenticeship.

cs.HC↗

arXiv2026

Inverse Rendering for Modeling with Line Primitives

Faithfully capturing diverse real-world objects with fuzzy, anisotropic structures, such as hair, fur, fibers, and textiles, for efficient real-time visualization remains challenging. Recent radiance field reconstruction methods capture these structures from multi-view images using translucent volumetric primitives such as 3D Gaussians rather than opaque low-dimensional primitives (e.g., triangles, line segments, and polylines), thereby limiting compatibility with standard depth-tested rasterization, reflection modeling, and physical simulation. We present an inverse rendering method for reconstructing fuzzy geometry using explicit line segments, which are rasterized on a subpixel grid for anti-aliasing to reproduce a semi-transparent appearance. While straightforward to render, optimizing numerous line primitives to match target images poses a significant challenge. We address this by introducing a stochastic differentiable rasterizer for line segments that produces informative gradients with respect to vertex positions, attributes, and discrete connectivity. Experiments on synthetic and real-world datasets show that our method outperforms surface-based approaches in capturing fuzzy boundaries and achieves quality comparable to volumetric representations while relying entirely on explicit geometry. The resulting representation integrates seamlessly with standard graphics pipelines, enabling cross-platform rendering, various shading models, and physical simulation.

cs.GR↗