HairDeck: Animation-Ready Stylized Hair Cards from a Single Image
Yuze He, Yixiang Wei, Wang Zhao, Chuhao Chen, Yanning Zhou, Yong-Jin Liu
AI 速览：HairDeck 将风格化发型视为无序的发片集合，用集合扩散 Transformer 从单张图像一次性生成发片及其弹簧骨骼链，得到可直接导出的可动画资产。
The hair of stylized characters in games and avatars is built from hair cards: artist-placed ribbons whose geometry is the visible hair, animated by spring bones. Existing image-based methods produce realistic hair or a single fused mesh, while CHARM, the only generator of stylized hair cards, decodes a hairstyle autoregressively as a long sequence of control points. Observing that a card hairstyle is an unordered set of artist-defined primitives, we propose HairDeck, which generates it as one: each card is a single token, and a set diffusion transformer denoises all cards together. From a single image, HairDeck generates the cards and the spring-bone chains that drive them, yielding an animatable asset that exports to ready-to-use downstream formats. On CHARM's own test set, it lowers Chamfer distance by 44% and card-orientation error by more than half while running 33 times faster, and from single images it outperforms CHARM, image-to-3D pipelines and strand-based methods. Project page: https://hyzcluster.github.io/HairDeck
We revisit the role of appearance modeling in 3D Gaussian Splatting (3DGS) and show that limited expressiveness in view-dependent reflectance is a key driver of representation redundancy. In standard 3DGS, low-order spherical harmonics (SH) are used, restricting the splats' ability to model high-frequency directional effects, which is typically compensated by increasing the number of splats. We propose \emph{NRF-GS: Neural Residual Fields for Gaussian Splatting}, a hybrid representation that replaces per-splat SH-bases with a shared neural residual field. Each Gaussian encodes a compact set of appearance features and a lambertian base color, while a lightweight \emph{global scene-level MLP} predicts view-dependent residuals conditioned on viewing direction, distance, and per-splat features. This formulation enhances directional reflectance modeling by combining diffuse per-splat reflectance representations with a shared global function for high-frequency details, enabling both higher expressiveness and parameter sharing across splats. Our key insight is that by accurately capturing high-frequency directional reflectance, especially in specular regions, the GS-representation becomes more expressive, reducing the need for geometrically redundant splats. As a result, NRF-GS achieves comparable or better rendering quality while reducing the number of Gaussians by up to 50\%, and produces visibly improved specular and high-frequency details.
Facial micro-expressions are subtle, short-lived facial movements that reveal genuine human emotions. Despite the large advances in facial expression synthesis and animation, realistic micro-expressions remain challenging due to scarce annotated data and the extremely subtle nature of their motion. Existing dedicated micro-expression methods therefore often struggle with generation quality, robustness, and generalization. We present MEGMAG, a transferable framework for micro-expression generation via magnification that enables pretrained facial animation models to generate realistic micro-expressions without modifying or fine-tuning their backbones. The key idea is to transform subtle micro-expression dynamics into the domain of regular facial expressions, where pretrained animation models can effectively capture and transfer facial motion. A learnable Magnification Module amplifies subtle micro-expression dynamics, enabling a frozen facial animation backbone to transfer the amplified motion to a source identity without altering its original architecture or parameters. A DeMagnification Module then attenuates the generated motion to recover realistic micro-expression characteristics. This design allows MEGMAG to leverage diverse off-the-shelf facial animation models while remaining architecture-agnostic and fully transferable across backbones.
Patryk Bartkowiak, Marcin Marlewski, Paweł Wójcik, Jae Joong Lee, Dominik L. Michels, Bedrich Benes, Sören Pirk, Wojciech Pałubicki
Vegetation plays a central role in cities, serving functional roles such as balancing shade and sky exposure, shaping the microclimate, carbon sequestration, and providing acoustic screening. Furthermore, aesthetic choices in landscape styles (formal, modernist, picturesque) are important for citizens' well-being and should be considered in the design of urban landscapes. In this paper, we present a novel method for placing vegetation, ranging from shrubs to large trees, in 3D urban scenes. Our method uses individual lots as input and employs a conditioned Vision-Language Model (VLM) to generate a top-down rendering of vegetation, placing large trees, small trees, and shrubs on lawn areas. This rendering is then converted into a 3D scene by detecting individual plants and selecting appropriate 3D assets. To refine the proposed configurations, we define an optimization loop that keeps the VLM-generated positions fixed after a deterministic feasibility repair and uses CMA-ES to optimize per-instance scale transformations along with discrete vegetation asset substitutions. To define the objective function for the optimization, we evaluate the generated 3D scenes with efficient surrogate models of shade and path-based acoustic exposure. Furthermore, we render in-scene views at each optimization step to compute view-dependent perceptual descriptors, enabling the generation of both aesthetic and functional vegetation layouts for urban scenes.
Robotic manipulation of multi-state materials needs a simulator that handles large deformation, topology change, and two-way contact between tools and materials while staying fast enough to drive online reinforcement learning. We present MG-CD-MPM, a tool-centered localized extension of CD-MPM. The implicit Ginzburg-Landau phase-field damage system is solved on a local patch by preconditioned conjugate gradients using a geometric-multigrid V-cycle as the preconditioner. The background MPM discretization remains uniform: only the damage solve is localized. Two-way rigid-tool coupling converts MPM contact impulses into forces and torques on the tool, exposing material resistance directly to the policy. The resulting Gym-like simulator covers soft solids, brittle materials, granular media, and viscoplastic or semi-fluid media within a unified per-particle constitutive layer. We demonstrate cutting, scooping, and shoveling in six scenes, combining scripted tool motions with PPO policies trained on two cutting tasks.
Chen Su, Xingqun Qi, Xurui Hu, Bingkun Yang, Muyi Sun, Man Zhang, Zhenan Sun
Deriving plausible and dynamic Human Object Interaction (HOI) has gained tremendous progress in Embodied AI and Immersive AR/VR. While existing HOI methods have effectively explored human interaction with individual or fixed objects, they still show limitations in complex multi-object interaction scenes. Meanwhile, previous approaches focus more on the contact information held by human-object relation, overlooking the significance of the contact situation of various entities (i.e., object-object). To address these limitations, we propose UniffordInteractor, a Reasoning-then-Interacting framework that leverages unified affordance for realistic and long-horizon human and multi-object interaction modeling. At the reasoning stage, we devise a Unified Affordance Reasoner (UAR) that employs a fine-tuned 3D Multimodal Large Language Model for unified object-centric affordance prediction. At the interacting stage, we propose Hierarchical Interaction Generator (HIG), which formulates the interaction generation as a hierarchical (e.g., root to body to hand to objects) autoregressive prediction, conditioned on the obtained unified affordance. Extensive experiments demonstrate that UniffordInteractor surpasses SOTA counterparts in human and multi-object interaction generation.
Xinyu Chen, Song Wu, Dingbao Shao, Jiahang Li, Jiang Lin, Yuhang Liu, Duo Li, Jiaxin Hu 等 15 位作者
Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Edit, a video editor that couples a multimodal encoder with a pretrained video diffusion transformer. The encoder jointly processes source frames and the editing instruction, and learnable queries convey their semantic context to the generator through a trainable connector and cross-attention. A complementary visual pathway concatenates source-video latents with noisy target latents before patch embedding, supplying dense spatial and temporal information for content preservation. We train the model with flow matching and a three-stage image--video curriculum comprising semantic alignment, joint adaptation, and high-resolution refinement. To support training, we construct over 1.2 million instruction-conditioned video-editing records covering object addition, removal, replacement, and attribute modification. We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality. Comparative experiments show that SemantiScene-Edit effectively follows editing instructions while retaining source content. At inference, the model requires only a source video and a natural-language instruction.
Crowd simulation aims to generate agent behaviours that are rich, natural, and appropriate to their surrounding environment. Recent advances in Machine Learning have enabled such behavioural patterns to be learned directly from trajectory data, allowing simulation models to capture increasingly complex crowd dynamics. However, the capabilities of these systems remain strongly dependent on the type and diversity of the data used during training. When evaluating how well crowd simulators generalise to real-world datasets, most existing metrics focus primarily on trajectory-level differences rather than behaviour-level similarity. In this paper, we present Trajeform, a framework that trains a reconstruction VAE (Variational Autoencoder) on real trajectory data to learn a meaningful continuous embedding that preserves behavioural characteristics of real crowds. The trained encoder can then be applied to synthetic trajectories generated by simulators and used to compare the resulting embedding distributions. Additionally, the learned embedding can be used to analyse supplementary data, such as trajectories corresponding to a simulator's principal behaviours or captured data from controlled environments (e.g., cycling), providing insight into their ability to span the real behavioural embedding space. The framework compares data from three simulators (CCP, modCCP, and RVO) against ground-truth datasets and is evaluated quantitatively and qualitatively using latent-space analysis, distributional metrics, and trajectory generation through sampling from the aggregated posterior distribution. The results indicate that the learned latent space captures meaningful trajectory dependencies found in the real data, making the framework useful both as an additional measure of synthetic data believability and as a tool for informing the training of future trajectory models.
Generating editable computer-aided design (CAD) models from text requires selecting operations that produce the intended changes to the geometry constructed so far. An incorrect operation can compromise subsequent steps, making reliable multi-step generation challenging. Existing methods based on large language models (LLMs) primarily learn to predict commands, without explicit supervision of the geometric states those commands should produce. Inspired by world-action models, we introduce CAD-WAM, which couples geometry-conditioned action generation with next-state prediction. We define complete modeling operations as actions in a CAD domain-specific language (DSL) and encode surface point clouds of intermediate solids into compact latent states using a pretrained variational autoencoder. An LLM backbone generates the next action from the target description and current geometric state, while an auxiliary head on the same backbone predicts the next state to provide geometric supervision during training. We construct step-wise training trajectories from parametric CAD models in the DeepCAD dataset, pairing each trajectory with an expert-level textual description. At inference, each generated action is executed by a CAD kernel, and the resulting solid is re-encoded to inform the next decision. On the DeepCAD test set, CAD-WAM outperforms the task-specific baselines in geometric accuracy and output validity, with larger gains on models requiring more operations, and produces more accurate geometry than general-purpose LLMs prompted with the same DSL. Ablations further show that next-state supervision reduces mean Chamfer distance by 13.2\% relative to an otherwise identical model trained only to predict actions. Code is available at https://github.com/Lzy-1037/CAD-WAM.
Triangle- and mesh-based neural rendering seeks to bridge neural representations and graphics engines through triangle primitives compatible with standard rasterization. Yet existing methods were developed and evaluated under inconsistent settings, with few direct comparisons and little evaluation in graphics engines. We introduce MeshSuite, the first benchmark for systematic evaluation of engine deployment and Non-Engine Render in triangle- and mesh-based neural rendering. MeshSuite evaluates two deployment settings: Standard Deployment, using an opaque mesh pipeline with vertex colors and hardware Z-buffering; and Dedicated Deployment, using method-specific engine implementations to preserve appearance and compositing features. For mesh splatting, we also audit exported surfaces for topological and geometric integrity as downstream graphics assets. Our results show that engine deployment reduces image quality in both settings; among evaluated methods, MeshSplatting degrades least under Standard Deployment. Dedicated Deployment preserves most fidelity at roughly 5-33x the runtime of Standard Deployment across two deployment datasets. For mesh splatting, explicit connectivity and shared indexing remain limited: shared vertex indexing alone guarantees neither manifoldness nor global connectivity. These findings show that rasterizability is only part of graphics readiness and underscore the need to assess engine deployment. Project page: https://x-up-lab.github.io/research/meshsuite.
We present new representations of Gauss--Legendre polynomials and their derivatives in the shifted power basis and in bases related to symmetric orthogonal Jacobi polynomials. Using these representations and certain recurrence relations, we propose $O(n^2+dn)$ methods for evaluating a Gauss--Legendre curve of degree $n$ in $\mathbb E^d$. We also propose algorithms for multipoint evaluation with computational complexity $O(Mdn+dn^2)$, where $M$ is the number of evaluation points. Numerical tests show that the approach using the shifted power basis is very fast, but it works well only for small $n$ (approximately up to 20), whereas the method employing Jacobi polynomials gives good numerical results even for large $n$ (around 100).
It is well-known that a $d$-dimensional polynomial Bézier curve of degree $n$ can be subdivided into two segments using the famous de Casteljau algorithm in $O(dn^2)$ time. We show that it is possible to do this in $O(dn\log{n})$ time using the fast Fourier transform and its inverse. The direct application of the method is numerically unstable for larger values of $n$. Therefore, we also introduce a modified version based on parameter scaling, which retains the same asymptotic computational complexity. This scaling approach can improve numerical accuracy in some cases (for $n\lesssim 50$), as demonstrated by our experiments. Moreover, the new method has a nice property: if a Bézier curve is extended by an additional control point, the subdivision can be updated in $O(d)$ time. The method can be adapted to subdivide rational Bézier curves and rectangular Bézier surfaces, as well as to compute the derivatives of Bézier curves. Although motivated by applications in CAGD, the proposed methods may also be useful in other areas involving polynomial reparameterization.
Yupeng Xiao, Yankai Wu, Jianjie Luo, Jiajie Lin, Yi Yu, Zhenguo Yang
Recent diffusion models have achieved impressive progress in single image to multi-view generation by synthesizing consistent novel views efficiently. Existing methods usually explicitly or implicitly couple geometry inference and texture synthesis, making generation vulnerable to inaccurate geometry prediction. Although several geometry-guided methods have explored decoupling, they usually treat predicted geometry as a fixed condition and do not explicitly model whether the geometry is reliable. We therefore propose Reliability-Guided Multi-View Generation (RGMV), which comprises a parameter-free Self-Consistency Geometry Gate (SCG) and a Reliability-aware Geometry Injection (RGI) module. Specifically, a geometry proposal generator produces multiple geometry predictions for the same reference image and target viewpoints under different stochastic sampling configurations. SCG measures their spatial agreement to derive a reliability map, which RGI uses to modulate the injection strength of geometry-conditioned residuals in the RGB synthesis model. Moreover, RGMV can adapt to different geometry proposal generators. As a result, RGMV preserves stronger geometric guidance in stable regions while attenuating unreliable geometry cues before they propagate into multi-view RGB generation. Extensive experiments show that reliability-aware geometry injection improves the geometric quality of multi-view generation. Code: https://github.com/WISLab-GDUT/RGMV.
Ronghuan Wu, Wanchao Su, Kede Ma, Jing Liao, Rafał K. Mantiuk
High-dynamic-range (HDR) formats and displays are becoming increasingly prevalent, yet state-of-the-art image generators (e.g., Stable Diffusion and FLUX) typically remain limited to low-dynamic-range (LDR) output due to the lack of large-scale HDR training data. In this work, we show that existing pretrained diffusion models can be easily adapted to HDR generation without retraining from scratch. A key challenge is that HDR images are natively represented in linear RGB, whose intensity and color statistics differ substantially from those of sRGB-encoded LDR images. This gap, however, can be effectively bridged by converting HDR inputs into perceptually uniform encodings (e.g., using PU21 or PQ). Empirically, we find that LDR-pretrained variational autoencoders (VAEs) reconstruct PU21-encoded HDR inputs with fidelity comparable to LDR data, whereas linear RGB inputs cause severe degradations. Motivated by this finding, we describe an efficient adaptation strategy that freezes the VAE and finetunes only the denoiser via low-rank adaptation in a perceptually uniform space. This results in a unified computational method that supports both text-to-HDR synthesis and single-image RAW-to-HDR reconstruction. Experiments demonstrate that our perceptually encoded adaptation consistently improves perceptual fidelity, text-image alignment, and effective dynamic range, relative to previous techniques.
Jun-Hao Wang, Changan Lu, Baoquan Chen, Peng-Shuai Wang
Existing neural 3D representations separately address continuous surface modeling, efficient image synthesis, or object-specific compression. They do not jointly provide the explicit surface queries required by ray tracing and a structured output space that can be predicted across objects. We introduce NeuOctree, a neural explicit representation that directly predicts whether and where a ray intersects a surface, together with its normal and albedo. NeuOctree stores shared features at the corners of sparse octree voxels and interpolates them into a continuous local field. A lightweight decoder with recurrent intersection-guided sampling refines each query near the predicted surface, enabling direct surface rendering without mesh extraction or texture baking and accommodating open, non-watertight geometry. The same octree structure supports both compact per-object optimization and a shared octree CNN that constructs renderable assets from colored point clouds without per-object fitting. Across seven meshes and four storage budgets, NeuOctree achieves the lowest positional error in 24 of 28 comparisons while retaining competitive normal accuracy, and substantially improves joint geometry--appearance reconstruction over N-BVH and VQAD. The feedforward model constructs a representation in 0.05 seconds and transfers from Objaverse to ShapeNet without retraining. We further demonstrate ray-traced rendering and image-conditioned generation, showing that NeuOctree provides a common output space for neural inference and direct surface rendering. Code is available at https://github.com/octree-nn/neural-octree.
4D generation, or dynamic 3D content generation, integrates spatial, temporal, and view dimensions to model realistic dynamic scenes, playing a foundational role in advancing world models and physical AI. However, maintaining long-chain consistency across both frames and viewpoints through the unique spatio-camera-motion (SCM) attention mechanism introduces substantial computational and memory overhead, often leading to out-of-memory (OOM) failures and prohibitive generation times. To address these challenges, we propose Turbo4DGen, an ultra-fast acceleration framework for diffusion-based multi-view 4D content generation. Turbo4DGen introduces a spatiotemporal cache mechanism that persistently reuses intermediate attention across denoising steps, combined with dynamically semantic-aware attention pruning and an adaptive SCM chain bypass scheduler, to drastically reduce redundant SCM attention computation. Our experimental results show that Turbo4DGen achieves an average 9.7$\times$ speedup without quality degradation on the ObjaverseDy and Consistent4D datasets. To the best of our knowledge, Turbo4DGen is the first dedicated acceleration framework for 4D generation.
Language models increasingly create 3D content through code, with practical workflows often relying on editing existing programs rather than regenerating them, for speed and precise control. Such edits require understanding what a program builds and foreseeing how a change reshapes it, yet prior benchmarks mostly evaluate generation or simple edits against a reference. In this paper, we introduce 3DCodeEdit, a benchmark of 2,592 instruction-guided editing tasks over 108 Blender programs that construct objects entirely in code. Its edits range from parameter to structural changes, style redesign, and scene composition, and are judged from renders against human-reviewed checklists. Across 17 models, the average success rate is only 37%, and even the strongest models complete fewer than 60% of structural and style edits. As models become more capable, their failures shift to geometric foresight, where the code runs but builds the wrong shape. We therefore study whether multi-turn feedback can compensate for this missing foresight. Feedback from a separate reviewer model that describes how the renders differ from the request helps most, more than maximum reasoning effort or an agent harness, and can let a 27B model surpass the single-shot rate of a 2.8T-parameter model.
Yilin Yao, Bin Lu, Xinhui Li, Meng Jin, Xinbing Wang
Dynamic web interactions continuously evolve as new edges arrive, offering large language models (LLMs) great potential for reasoning over edge-level temporal semantics. However, continuously arriving edges are often repetitive, obscuring the key structures that truly reflect temporal changes and limiting the efficiency of LLMs. A key insight is that beneath the dense stream of edge arrivals lies a sparse evolution of temporal intent, enabling LLMs to be responsively triggered only when such intent shifts, while otherwise inheriting the previous state. To this end, we propose a hypergraph-based framework named HyperIntent, which enhances dynamic graph reasoning by parsing differentiated intents, quantifying their temporal discrepancies, and performing shift-triggered semantic updating. Experimental results on destination node retrieval and future link prediction demonstrate that HyperIntent consistently achieves the best overall performance, with average relative Hits@1 gains of 34.0% and 25.4% over vanilla TGAT and DyGFormer, respectively. Meanwhile, HyperIntent reduces LLM token consumption by 54.4%-95.9% across six datasets while achieving leading predictive performance. In-depth studies further confirm the necessity of shift triggering for identifying structural and semantic temporal changes. The code will be made publicly available upon completion of the review process.
Neural representation has emerged as a powerful tool to mitigate the demand for increasingly higher-resolution images. However, a fundamental trade-off remains between structured (e.g., feature grids, which offer spatial consistency) and unstructured (e.g., 2D Gaussians, which adaptively capture local variances) representations. This choice significantly affects how well an algorithm represents an image. Here, we present Gaussian-Grid, a hybrid neural representation that adaptively integrates structured and unstructured primitives. By dynamically partitioning computation between 2D Gaussian distributions and their attached multi-resolution feature grids based on the local frequency content, Gaussian-Grid automatically optimizes its architecture to match the underlying complexity of the target signal. We evaluate our approach using high-resolution natural images and demonstrate its applications as GPU-native dataset storage, showing $1.24$-$2.14\times$ decoding speed improvement and PCIe data traffic reduction of $23\times$-$176\times$ over JPEG + ZIP data archiving. The results show that Gaussian-Grid maintains small memory footprints while achieving high reconstruction fidelity across a variety of datasets.
Donggang Jia, Alexandre Kouyoumdjian, Ondrej Strnad, Uros Smajdek, Ciril Bohak, Dilara Bayraktar, Alejandra Esparza, Peter Mindek 等 11 位作者
Biological models bring the molecular world of cells into view, enabling experts and the public to explore and understand biological discoveries. Building mesoscale models requires organizing many molecular components into spatial arrangements that can be revised as evidence and interpretations develop. We present MesoCraft, a browser-based tool for structural biologists centered on interactive procedural modeling. User-defined rules generate collections of molecular instances, while a geometry-node editor and visual feedback support authoring and revision of their spatial relationships. Beyond model construction, MesoCraft integrates exploration and communication, enabling researchers to inspect molecular organization and create guided animations that highlight key spatial relationships. By connecting modeling, exploration, and explanation in a unified workflow, MesoCraft allows insights gained during exploration and communication to inform iterative model refinement, supporting the development of mesoscale models into visual narratives of cellular organization. A demonstrative deployment of the tool is available at https://mesocraft.kaust.edu.sa/.
Allen Tu, Meher Gitika Karumuri, Shifan Zhu, Hailing Wang, Yen-Liang Lin, David Held, Matthias Zwicker, Tom Goldstein
Robots, autonomous vehicles, and consumer 360° cameras use fisheye lenses for wide fields of view (FoVs). Simulation-ready digital twins require photorealistic rendering across the full FoV and an aligned collision mesh. However, prior fisheye Gaussian Splatting methods only rasterize up to 180° and do not produce meshes, while Gaussian surface reconstruction methods assume pinhole training images. We present GOFish, the first 3D Gaussian Splatting surface reconstruction method to train directly on fisheye images, rasterize FoVs beyond 180°, and extract an aligned mesh from the same scene. Calibrated densification recovers the surface across the entire FoV, while Calibrated Mip Filtering and Wide-Angle Appearance Modeling keep pixel blur and lens falloff out of the scene and mesh. Across the 200° FIORD and 205° FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real time, ray traces the full FoV, and provides collision geometry.
Honeycomb-Surfels: Compact Hexagonal Primitives for Efficient Scene Rendering and Reconstruction
Prakhar Kaushik, Soumava Paul, Alan Yuille
AI 速览：提出六边形平面表面图元 Hexels，以更少更大的图元与一阶颜色场提升渲染与重建的内存效率。
Novel view synthesis and 3D reconstruction have advanced rapidly with radiance fields and splatting, yet their memory and computational demands limit their use in resource-constrained settings. We introduce Hexels: novel planar hexagonal surface elements with a first-order spatial color field for efficient 3D scene and 2D image representation. Hexels exploit locally planar regions with smoothly varying appearance using fewer, larger primitives. Their hexagonal support provides an area- and boundary-efficient domain, while the local color field captures appearance variation within each primitive. Across 34 scenes, Hexels improve PSNR over memory-matched 2DGS by 4.53-7.38 dB across datasets while requiring lower training VRAM. We further ablate Hexels by showing that per primitive, Hexels outperform 2DGS by upto 4.74 dB while using lower training VRAM and having 2.31 times faster FPS. Even in unconstrained settings, Hexels offers complementary quality-memory trade-offs. For efficient 3D surface reconstruction, Hexels reduce error by 28.9% compared with memory-matched 2DGS across 15 DTU scans. On 6 Tanks and Temples surface scenes, Hexels achieves the highest mean F1 among compared methods (34.38%, versus 31.72% for 2DGS). We further also show that Hexels, as a general primitive, improves 2D image representation over Image-GS. Hexels push the Pareto frontier on the quality-efficiency trade-off of surface-aligned primitives.
Haruki Fukuda, Constantin Lux, Wolf Schweitzer, Tadashi Hosoya, Lars Ebert
Photorealistic volume rendering of computed tomography (CT) may convey anatomy intuitively, but it needs the CT data, dedicated software and a powerful graphics processor. Three-dimensional Gaussian splatting (3DGS) displays photorealistic scenes in real time on less powerful devices, but existing medical approaches train each scene for minutes to hours. We aimed to convert a CT volume rendering directly into a 3DGS model without training. Splats were placed on an adaptive octree of millimetre-sized cells. Their colour and opacity were computed on the graphics processor with the renderer's lighting, cell opacity was converted analytically into splat opacity, and faint splats were retained stochastically above the 1/255 opacity floor of standard 3DGS rasterisers. Six public CT scans (1.5-mm voxels) were converted with three transfer functions and compared with converged references by peak signal-to-noise ratio (PSNR), structural similarity and a learned perceptual metric. Two-million-splat models reached a PSNR of 32.6 +/- 0.9 dB for bone, 30.1 +/- 1.0 dB for contrast-enhanced vessels and 32.3 +/- 1.5 dB for skin and bone. A conversion pass (usually two or three per model) took 4-30 s on a current laptop and 9-60 s on a desktop with a 4-GB graphics card. On the laptop, the models rendered at 68 frames per second at 800 x 800 pixels. Training-free conversion produced real-time 3DGS models of photorealistic CT renderings in about a minute or less per conversion pass on ordinary computers, with fidelity that we consider sufficient for interactive viewing.
Anastasiya Pechko, Agata Szymańska, Piotr Borycki, Joanna Szelewska, Michał Mnich, Sławomir Tadeja, Przemysław Spurek
The growing need for high-quality 3D content remains among the most prevalent challenges in virtual reality (VR). Recently, novel view synthesis techniques have enabled rapid content creation, with 3D Gaussian Splatting (3DGS) emerging as one of the most promising methods for generating high-fidelity 3D scenes. However, using 3DGS to reconstruct unbounded outdoor scenes can leave horizons incomplete due to missing data or a lack of distant geometric constraints. Such incompleteness can break immersion, compromising VR's core characteristic. A typical workaround in traditional VR systems is a textured cube (skybox) or sphere (skydome) surrounding the environment to create the illusion of a distant background. However, these approaches struggle to blend seamlessly with the reconstructed 3DGS geometry. To address these issues, we introduce the DomeGS framework, which incorporates the skydome concept not as a flat background shell, but as a part of the reconstructed Gaussian Splatting 3D scene. By integrating the horizon and sky into the 3DGS representation, our method provides a unified, scene-consistent environment bridging foregrounds and backgrounds for greater realism. Experimental evaluation with N=36 users demonstrates that DomeGS significantly increases spatial presence (p=0.042) and realism (p=0.042) compared to the skybox baseline.
Inserting virtual objects into scenes reconstructed via 2D or 3D Gaussian Splatting (2DGS/3DGS) is a key capability for content creation, AR, and synthetic data generation. However, realistic insertion requires the object to be seamlessly lit by the scene and, conversely, to cast physically plausible shadows onto it. Existing approaches either harmonize appearance in image space, sacrificing multi-view consistency, or approximate scene illumination with an environment map at the insertion point, which cannot represent near-field area lights such as windows and lamps that dominate indoor lighting. To address this, we present GraftRay, a unified method for physically consistent insertion of mesh objects into indoor scenes represented by either 2D or 3D Gaussians. We recover scene materials and surface normals with diffusion-based priors, and explicitly model spatially-extended emitters, solving their radiance in closed form. A hybrid renderer then jointly path-traces the scene Gaussians and inserted meshes, producing view-consistent shading, interreflections, and soft shadows. On FIPT, GraftRay sets a new state of the art, outperforming recent baselines such as ComGS and EAG-PT in rendering fidelity and shadow accuracy, while maintaining highly competitive rendering efficiency.
We present a new MPM-based solver for perfectly elastic solids, which can recover from strong deformations and naturally allows contact handling. Typical MPM solvers suffer from numerical fracture, where objects break apart even when no plasticity is included in the elasticity model. Previous approaches for perfectly elastic solids solve this by computing forces on a fixed grid in the reference configuration. However, this makes them incompatible with standard collision handling methods. We instead propose a method for deformable solid simulation without numerical fracture, that still computes elastic forces on the current grid. Our method can easily include grid-based collision handling, as commonly used in MPM frameworks. Additionally, we compute the deformation directly from the particle positions and a fixed reference grid, without any updates based on velocity gradients. This facilitates the inclusion of particle-level collision handling and zero-energy mode suppression, which counteracts artifacts from non-linear deformations. As a result, we can efficiently simulate complex scenarios of elastic objects undergoing large deformations, including contact.
Walk-on-Spheres (WoS) enables mesh-free solutions of elliptic PDEs, but conventional methods repeatedly discard sampled trajectories, limiting their efficiency in interactive applications. We introduce reusable transport skeletons, a persistent representation that organizes stochastic walks into a shared computational graph. Our framework enables efficient reuse across spatial queries and boundary conditions, while supporting progressive refinement, structure-preserving compression, and local adaptation to dynamic geometry. It further enables interactive editing of transport between selected regions. Experiments demonstrate substantial computational savings while preserving solution accuracy. By transforming transient stochastic trajectories into reusable transport structures, our method provides a flexible foundation for interactive Monte Carlo PDE solving.
Da Li, Yihan Zang, Ivan Viola, Zeyu Wang, Dominik Engel
Achieving high-quality reconstruction and responsive camera tracking remains challenging for monocular RGB Gaussian splatting SLAM under a limited optimization budget. We present an online system that addresses this challenge by combining learned geometric priors with explicit geometric camera tracking, which can substantially outperform learned camera prediction in terms of tracking accuracy. A frozen streaming 3D foundation model serves as a virtual RGB-D sensor, providing depth estimation from monocular video. An explicit geometric solver then estimates camera motion through feature reprojection, and a streaming Gaussian mapper integrates the resulting observations into a dense scene representation. We further show that geometric tracking improves camera pose accuracy across representative streaming geometry backbones, demonstrating the broader applicability of our approach. Extensive experiments on indoor and outdoor datasets show that our method significantly improves reconstruction quality over existing approaches, achieving about 7 FPS on a single RTX 4090 and delivering 2-3 times the throughput of the evaluated prior SLAM systems.
Saar Huberman, Ron Mokady, Or Patashnik, Daniel Cohen-Or
In modern generative models, images are specified and controlled through text prompts. In practice, images are generated from sequences of tokens derived from these prompts. However, the space of token sequences lacks a consistent accessible structure: semantically similar images may correspond to sequences that differ in wording, ordering, and placement of concepts, while similar token sequences may encode very different semantics. This apparent lack of structure makes it difficult to perform smooth transitions in this space, hindering applications such as image blending and continuous control of edits. We argue that this limitation stems not from the absence of semantic structure, but from misalignment between representations. To address this misalignment, we introduce Token-to-Token alignment, a framework that establishes explicit semantic correspondence between tokens across prompts. Our approach transforms prompts into a structured representation in which semantically corresponding concepts are mapped to consistent positions across prompts, and then aligns their token embeddings based on semantic similarity. Concretely, the method consists of two stages: a structural alignment that rephrases prompts into a shared structured form, followed by an embedding-level alignment that matches token representations across prompts. With this alignment in place, simple linear interpolation becomes a meaningful operation, producing smooth and coherent semantic transitions and enabling applications such as blending and continuous editing. Our results show that text embedding spaces in text-to-image models implicitly encode a continuous semantic structure that becomes accessible once representations are properly aligned, suggesting that semantic control can be achieved by organizing existing representations rather than modifying the generative model.
Donggang Jia, Da Li, Zainab Alsuwaykit, Sebastian Hartwig, Dominik Engel, Timo Ropinski, Ivan Viola
Long-horizon object-centered tasks in interactive 3D environments involve heterogeneous events that unfold across time and space, including fine-grained manipulations, larger structural motions, and subtle state transitions. Effectively visualizing such processes is essential for helping users understand how actions lead to outcomes over time. However, existing camera control methods in 3D systems are commonly designed for static objects, and often fail to maintain the legibility of task-relevant components and their evolving relationships throughout long-horizon executions. We present VISTA, a differentiable viewpoint planning framework for long-horizon task visualization. We formulate camera control as a visualization policy that optimizes camera parameters under a unified rendering-based quality objective and we propose five quality metrics to satisfy our five design goals. To support both local part inspection and interaction-centric visualization centered on a primary object, our formulation handles targets defined either as object--part pairs or as relational tuples involving another interacting object. This allows the planner to reveal not only what part is important, but also how it functionally relates to surrounding structures during task execution. We demonstrate that the same planner supports two complementary use cases: presentation-driven narrative visualization for communicating how long-horizon tasks unfold to human viewers, and agent-driven active observation within an observe-act loop. Together, these results position differentiable viewpoint planning as a general mechanism for ``how-things-work'' visualization of long-horizon object-centered interactions in 3D environments.