ALT Additive manufacturing, also known as 3D printing, is significant for its use in industries such as aerospace, robotics, medicine, and construction. However, digital mesh files remain vulnerable to geometry defects which can be introduced through malicious tampering or designer error. This can lead to hampered structural integrity and impede its use due to unexpected degradation of the part. To address these challenges, we propose SAMS, a multi-layered hierarchical graph framework designed to analyze meshes to enhance the Security of Additive Manufacturing Systems. SAMS captures geometric and topological information across mesh, facet, and surface levels, providing an adaptable architecture for targeted mesh processing tasks. We evaluate SAMS's accuracy against existing mesh processing tools. The graph construction results in rich structural representation and has the potential to significantly reduce downstream analytical complexity when detecting vulnerabilities. This paper presents

ALT Unit quaternions are widely used in computer graphics making their efficient compression critical for reducing memory consumption and bandwidth requirements. In this paper, we present a novel method for encoding uniformly distributed unit quaternions. Our thorough analysis of several quaternion parameterizations compares their theoretical error properties, quantization behavior, computational cost, and memory efficiency and provides both an analytical and empirical characterization. We show that an infinity-norm parameterization with our optimized warp function consistently offers the smallest maximum error and the best storage efficiency, at a decompression cost equal to or lower than that of parameterization methods found in practice. We demonstrate the practical benefits of the proposed approach in several graphics applications, including tangent-space normal mapping, skeletal animation with blend weights, and Gaussian splatting.

ALT Sketching is one of the most natural ways for people to express visual ideas. Unlike the text prompts that most generative systems rely on today, a sketch captures spatial structure and intent directly, including where a designer wants highlights and shadows to appear. Yet current sketch-to-3D pipelines have no input for that intent: they bake the drawn shading into the texture as dark paint, and the light cannot be moved. We propose a system that transforms a rough garment sketch, marked with sparse annotations (a star for a highlight and a moon for a shadow), and a short text prompt describing the texture, into a textured 3D mesh with an explicit directional light, as a scene that can be relit and edited after generation. The two symbols are the most frequent proposals in an elicitation study with art students. From the marked sketch the system predicts the scene's directional light and, separately, a displacement field that bends the surface so that the moon-marked regions fall into

ALT Displacement maps encode fine geometric detail in compact textures and simplify surface authoring, but using them typically requires pre-tessellating surfaces into dense triangle meshes, incurring a heavy memory cost. Recent tessellation-free methods enable direct ray tracing. However, broader geometric queries such as surface sampling, specular manifold search, and closest-point queries remain less explored. Supporting these operations requires surface enclosures, area estimates, and normal bounds, which scalar height intervals alone do not fully provide. We present a first-order texture-space hierarchy that unlocks these queries directly without pre-tessellation. Each node stores a local affine height plane and a conservative residual bound, retaining spatial height variation that scalar minimum–maximum bounds discard. The plane and residual provide surface enclosures for intersection and distance searches, while the plane's height and slopes supply area estimates for sampling. Addit

ALT Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and computation remains challenging. Recent 3D Gaussian Splatting (3DGS) systems, such as V³, enable efficient mobile streaming by encoding time-varying Gaussian attributes with commodity video codecs and hardware decoding. However, existing pipelines treat all Gaussians as frame-varying signals, which is poorly suited to full-scene content where large portions remain temporally stable. Our empirical study shows that frame-wise adaptation and video coding of all Gaussians cause quality degradation in complex full-scene content, particularly in temporally stable regions. To address this issue, we propose ResStreamGS, a residual-sensitive dual-stream framework that processes Gaussians according to their temporal behavior. ResStreamGS identifies residual-sensitive primitives through lightweight probe adaptation and reuses stable Gaussians withi

ALT We study the problem of learning diffusion-based world models that jointly capture scene geometry and visual appearance in complex first-person environments. Existing approaches typically train a single action-conditioned model that predicts future RGB frames, but they either ignore geometry or require action supervision for all modalities, limiting scalability to large unlabeled video datasets. In this work, we introduce a two-stage diffusion world model that separates geometric and appearance prediction while maintaining joint temporal coherence. Our key idea is a staggered RGB-depth formulation: depth at time t+1 is predicted first using an action-conditioned diffusion transformer, and the RGB frame at the same timestep is then generated conditioned on this predicted depth using a second diffusion model without action inputs. We show that this decoupled design faithfully approximates a hypothetical joint RGBD diffusion model trained with diffusion forcing, enabling the second stage

ALT Contact-rich inverse problems require efficient gradients and reliable enforcement of non-penetration. Existing differentiable methods provide fast simulation and rigid-deformable coupling, but contact treatments based on penalties or velocity-level constraints can permit penetration or positional drift. Incremental Potential Contact (IPC) maintains non-penetration through geometric barriers and continuous collision detection (CCD). However, repeated forward and backward evaluations remain costly in full-space finite-element (FEM) implementations, particularly when nearly rigid components retain many vertex degrees of freedom (DoFs). We present DiffGIPC, a GPU-accelerated differentiable IPC framework for optimizing interacting deformable and nearly rigid bodies. Our affine-body derivatives enable rest-shape design, initial-state optimization, and articulated control while retaining only twelve motion DoFs per affine body. These gradients account for how changes in geometry, placement,

ALT In this paper, we formulate a novel form of conditional layout generation for graphic design, termed content-edited layout generation(CELG). CELG extends conventional content-aware layout generation (CLG), which generates a foreground layout for a fixed background image. The key distinction is that CELG treats the input image as an editable visual asset and, conditioned on a textual design intention and a foreground requirement that may be empty, produces an adapted background together with its layout. To study this task, we introduce Search-Compare-Edit(SCE), a data construction framework that supplies the paired supervision missing from existing design datasets. Starting from professional design canvases and their layouts, SCE synthesizes plausible pre-edit source images and corresponding editing instructions, yielding InstructEdit, a dataset of aligned source images, target canvases, instructions, and layouts. Based on the generated data, we train a two-flow multimodal flow matching

ALT We present a method for constructing a simple quadrangulation of the sphere from an unordered set of n direction vectors, with application to parametric volumetric models of skeletal joints. We first select a quadrangulation encoding the joint topology by exhaustively enumerating candidate dual graphs, prioritizing convexity and regularity of the resulting quads. We then realize this topology on the sphere without crossings by solving a conflict-constrained assignment problem with lazy cyclic-order separation, and recover the corresponding primal graph. Experiments on direction sets from spherical Fibonacci sampling of size 3 to 20 show that the method robustly produces valid, high-quality quadrangulations across a range of input configurations. The resulting primal graph is directly converted into exact rational Bézier shell and column volumes, yielding a compact and precise volumetric representation of the joint. The approach extends naturally to multi-joint assemblies, enabling the

ALT In this paper, we introduce TempFit, a novel approach for the problem of Panoptic Symbol Spotting on CAD drawings. Existing methods treat symbol spotting as a per-primitive classification or mask-prediction task, with no explicit constraint enforcing that the primitives assigned to one instance form a spatially connected group. As a result, predictions may erroneously merge primitives from distant symbols into a single instance or fragment a coherent symbol across multiple predictions. Motivated by the professional "retrieve-and-transform" CAD design workflow, we reformulate this task as a Template Fitting problem, where each foreground instance is detected by retrieving and aligning a canonical template from a library, enforcing structural completeness by construction. Building on this formulation, we present an autoregressive transformer that unifies template retrieval and pose prediction within a single end-to-end pipeline. The framework treats the template library as an explicit ge

ALT The design of ergonomic products and workspaces requires a deep understanding of human anatomy and posture. In particular, the head-neck-shoulder region is critical for many applications such as cervical orthoses, helmets, and other head-worn or neck-worn devices. This paper presents a framework that integrates posture neutralization with statistical shape modeling (SSM) for the head-neck-shoulder (HNS) region. We acquire a single three-dimensional (3D) scan of the head-neck-shoulder region from each of 200 Chinese adults in a natural posture for each subject. Every scan is then posture-neutralized before SSM. We fit a parametric head-neck model to each scan to separate cervical pose from anatomy, and reset the cervical pose to a common neutral configuration, so that the resulting model captures anatomical shape variation rather than posture-induced deformation. The resulting shape space yields a standardized template and population-wide modes of variation, providing baselines for para

ALT Holographic devices must reconstruct an entire light field given a finite number of spatial light modulator (SLM) pixels, and must do so using scalar-wave diffraction from coherent illumination alone. As a result, computer-generated holography (CGH) often has speckling artifacts. We propose HoOPOE (Holography-Optimized Perceptual ObjectivE function), a CGH-specific, perceptually-aware loss function that hides speckles from human vision. HoOPOE is effective for CGH optimizations because it accounts for the human visual system's sensitivity to spatio-temporal frequencies and color, and its parameters are tuned to maximize its performance as a loss function for CGH. We use HoOPOE to generate holograms for mesh-derived focal stacks. We then conduct a user study to benchmark HoOPOE-generated holograms against existing loss functions. HoOPOE yields the most visually pleasing results; users prefer HoOPOE 68% of the time over the next best objective function on average.

ALT Sprite-based animation remains prevalent in video game development, but manual in-betweening—generating intermediate frames between keyframes—is labor-intensive. Existing video frame interpolation methods, optimized for photorealistic content, introduce smoothing artifacts when applied to the sharp edges, limited color palettes, and stylized motion characteristic of hand-drawn sprites. We present a deep learning approach for sprite in-betweening that adapts a U-Net encoder-decoder architecture with domain-specific modifications, including RGBA-aware processing, multi-scale flow refinement, and alpha-gated blending to preserve pixel-perfect sprite aesthetics. Following data-centric AI principles, we demonstrate that a few carefully curated sprite animation triplets—selected for diversity, quality, and representative edge cases—enable effective training without requiring massive generic datasets. Experimental evaluation using the FLIP perceptual metric shows that our method achieves a me

ALT Projected Walk on Spheres (PWoS) solves surface PDEs through ambient sampling and closest-point projection, but tightly folded embeddings can force short steps or ambiguous projections despite simple intrinsic geometry. We introduce Walk-on-Ambient-Patch (WoAP), which treats the embedding as a solver variable. Edge-isometry constraints and tangent-point repulsion separate intrinsically distant regions, optionally in higher codimension; patch states track residual projection branches without introducing PDE boundaries. We analyze the resulting geometry–sampling tradeoff: additional dimensions provide separation freedom but reduce uniform tangent progress, while strong tangent-biased proposals incur large importance-weight moments. Folded-surface experiments demonstrate reduced projection conflicts, a separate hard-constraint pilot verifies metric preservation, and an internal fixed-RMSE comparison favors higher dimensions at stricter accuracy targets. Together, these results motivate ch

ALT Topology optimization has found significant utility in additive manufacturing, but the resulting geometries are often difficult to realize using subtractive methods. The complex, curved surfaces of optimized designs typically require time-consuming point milling operations. In contrast, geometries composed of developable surfaces are well suited to flank milling, enabling substantial improvements in machining efficiency and surface quality. However, globally minimizing Gaussian curvature leads to overly simplistic designs. We address this challenge with a novel method that represents geometry using three implicit neural fields, optimized to produce machinable components. The resulting designs consist of regions of developable surfaces separated by seamlines where curvature is concentrated. Seamlines are efficiently computed as intersections of the implicit fields, while the geometry emerges through their product. By constraining surfaces to align with the cutter's normal or tangential

ALT We present Co-DSL, a framework in which users and LLMs co-design reusable spatial concepts for layout generation. In the first stage, our system allows users to create a library of personalized operations. To add a concept, a user provides a description, which the LLM converts into an initial implementation. The user can then observe the concept's execution behavior over example scenarios and provide lightweight feedback to the LLM, which revises the concept's implementation logic. In the second stage, given a library, an LLM can author a layout program that meets a new layout request from a user. Executing a layout program instantiates a concept-graph, a declarative composition of library concepts. In this way, we require the LLM only to specify which concepts should hold, while we use a solver to search for a layout that satisfies them. Across five spatial layout domains, we find that Co-DSL libraries let LLMs capture layout patterns that are challenging for prior fixed-operation sys

ALT Recent artisan mesh generation methods produce meshes whose topology comes close to the preferences of human artists. But gaps to artist quality remain because generated meshes commonly contain holes, stray faces, and duplicated faces. Regenerating a mesh from scratch is costly, and a new draw may change the preferred topology entirely or introduce errors elsewhere. In this work we study artisan topology correction. The task is to correct the topology of a generated mesh (the draft) in place, toward the layout an artist would make. We propose a method for this task. It is a face-level correction network based on a prefix-LM. It reads the mesh bidirectionally and emits an edit sequence autoregressively. The sequence consists of a deletion phase followed by an addition phase. We propose the novel formulation of the deletion head which picks faces to delete autoregressively and the addition head that selects vertices to form a face to add. The addition head also adds vertices when needed.

ALT The rapid progress of 3D generative models has made 3D asset creation increasingly accessible. However, achieving intuitive, precise, and interactive user control, such as point-based drag editing, remains challenging. Existing 3D drag methods either rely on slow 2D diffusion priors, such as Score Distillation Sampling, or directly warp geometry, often causing multi-view inconsistency and topological artifacts. In this paper, we present 3DRAG, a training-free framework that executes precise drag editing natively within the explicit 3D latent space of large-scale generative models, without SDS-based optimization or iterative point tracking. To ensure geometrically plausible deformations, 3DRAG explicitly extends the drag displacement field into 3D space, incorporating an Occupancy-Aware Winner-Takes-All (OA-WTA) strategy to robustly handle sparse geometries. Furthermore, we introduce a generative editing pipeline that explicitly transforms latent representations via the 3D displacement

ALT Geometric and topological features, such as isosurfaces, quantiles, merge trees, and Morse-Smale complexes, are central to the analysis and visualization of scientific data across diverse domains, including medical imaging, climate science, materials science, and astronomy. However, most lossy compressors for scientific data provide only pointwise error guarantees and do not preserve derived features. Existing feature-preserving compressors are often difficult to develop and typically tailored to a single feature. In this paper, we introduce FeatureZ, a lossy compression framework for structured volumetric scalar fields that can preserve a wide class of geometric and topological features. In particular, FeatureZ frames feature-preserving compression as preserving pointwise upper and lower bounds together with a consistent classification of each point's star (i.e., its incident cells) in the underlying structured mesh. Although not all notions of feature preservation fit this framework,

ALT Subdivision surfaces represent free-form geometry through a sparse control cage, but recovering that cage from a dense mesh is not merely a fitting problem: the system must infer where control is needed, which curves encode design features, and when an initial result should be revised. We present SubDGuide, a modeler-inspired agentic workflow for this task. Stage A reads aligned multiview geometric evidence and produces a compact plan for cage resolution, feature mapping, and broad-form fitting. Stage B inspects the resulting surface, requests targeted diagnostics, and chooses repair, rollback, or stopping actions. Geometry tools execute and verify every change; the planner never generates vertices or connectivity. On a fixed evaluation cohort, SubDGuide improves all five reported metrics over automatic remeshing: median Chamfer-L1 decreases from 0.641% to 0.443% of the target bounding-box diagonal, and F-score at a 1% tolerance increases from 82.00% to 92.78%. Stateful feedback improv