We present SCOPE (Scale-Consistent One-Pass Estimation of 3D Geometry), a novel approach for estimating 3D geometry from extended monocular video sequences, where existing methods struggle to maintain both geometric accuracy and temporal consistency across hundreds of frames. Our approach generates affine-invariant 3D point maps with shared parameters across entire sequences, enabling consistent scale-invariant representations. We introduce three key innovations: viewpoint-invariant geometry aligning multi-perspective points in a unified reference frame; appearance-invariant learning enforcing consistency across exponential timescales; and frequency-modulated positioning enabling extrapolation to sequences vastly exceeding training length. Experiments across diverse datasets demonstrate significant improvements, reducing relative point map error by 24.2% and temporal alignment error by 34.9% on ScanNet compared to state-of-the-art methods. Our approach handles challenging scenarios with complex camera trajectories and lighting variations while efficiently processing extended sequences in a single pass. Code and model weights: https://github.com/zhengzhang01/SCOPE.
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Text-to-image diffusion models generate images by gradually converting white Gaussian noise into a natural image. White Gaussian noise is well suited for producing diverse outputs from a single text prompt due to its absence of structure. However, this very property limits control over, and predictability of, specific visual attributes, as the noise is not human-interpretable. In this work, we investigate the characteristics of the input noise in diffusion models. We show that, although all frequencies in white Gaussian noise have comparable statistical energy, low-frequency components primarily determine the image’s global structure and color composition, while high-frequency components control finer details. Building on this observation, we demonstrate that simple manipulations of the low-frequency noise using low-frequency image priors can effectively condition the generation process to reconstruct these low-frequency visual cues. This allows us to define a simple, training-free method with minimal overhead that steers overall image structure and color, while letting high-frequency components freely emerge as fine details, enabling variability across generated outputs.
Due to its highly curved geometry, tightly coiled hair is challenging to model and edit using standard position-based tools. In this work we propose using material curvatures and twists to analyze and edit tightly coiled hair styles. Our method relies on the geometry of super-helices, primitives parametrized by piecewise constant curvatures and twists, whose helical geometry naturally resembles a coiled hair strand. Using this curvature/twist space, we introduce new editing tools that allow us to expand, shrink, “ruffle”, interpolate or guide the position of coiled hair in a natural way. We present analytical expressions for geometry and gradients that allow our method to run efficiently and without the need for any training data. We successfully apply our tools to highly coiled simulated hairs, as well as those generated procedurally.
Simulating free-surface flows requires capturing the effects of incompressibility, viscosity, and surface tension. Existing particle-based methods often rely on operator splitting, which introduces coupling artifacts and limits stability. We propose a unified nonlinear optimization framework that achieves a strong coupling of these three effects within a single solver. By leveraging peridynamics, we formulate the discretization of distinct fluid mechanisms under a consistent variational principle. Specifically, we recast fluid motion as a nonlinear variational optimization problem over particle positions, which is solved via the semi-implicit successive substitution method. Moreover, the framework incorporates separate treatments for bulk and shear viscosity, allowing for more refined control of different viscous fluid behaviors. To the best of our knowledge, this is the first particle-based unified solver capable of fully resolving the interdependence of incompressibility, viscosity, and surface tension, thereby significantly enhancing stability in complex simulations of free-surface flows. The source code for the paper is publicly available at https://github.com/peridyno/peridyno.
Smith microfacet models are widely used to describe the interaction of light with surfaces. However, conventional microfacet models account for only a single bounce on microsurfaces, which leads to energy loss. To address the problem of missing energy, several unbiased methods have been proposed to compute multiple bounces on Smith microsurfaces. In addition, a position-free multiple-bounce microfacet model has been introduced by assuming that each bounce of a light path is height-independent, leading to lower variance but biased results. In this paper, we propose a novel derivation of multiple-bounce Smith microfacet bidirectional reflectance distribution functions (BRDFs) based on a partial adoption of the independent-bounce assumption. Our model treats most bounces as height-interdependent, leading to a height-interdependent path formulation. We further propose a successive height preintegration to make this formulation position-free. Although our model still exhibits an extremely small bias, it achieves lower variance than prior unbiased methods. Furthermore, we introduce a path type-aware BRDF evaluation that further reduces variance.
Realistic integration of user-specified textures into scene images is a fundamental task in computer graphics and image editing. While existing material transfer and reference-guided inpainting methods can edit surface appearances, they often fail to address the specific requirements of texture tiling. This task necessitates precisely repeating a reference pattern according to user-defined parameters such as frequency, orientation, and scale. Furthermore, current generative approaches often struggle to maintain the structural fidelity of the reference texture, limited by either destructive pixel-level resampling or the lack of fine-grained spatial information in semantic image encoders, and they frequently fail to preserve the coherent lighting and geometry of the original scene. In this paper, we propose a novel framework for controllable and high-fidelity texture tiling based on Diffusion Transformers. Our approach introduces two key technical innovations to decouple spatial manipulation from content generation. First, we propose a Coordinate-Transformed Rotary Embedding mechanism. By applying 2D affine transformations directly to the relative positional embeddings between the target latent and the image condition, we achieve precise control over tiling patterns without explicit pixel warping, thereby utilizing the full information of the reference condition without degradation. Second, a Disjoint Attention Mask is employed to shield reference features from semantic leakage. This preserves structural integrity while seamlessly blending the synthesized texture with the scene’s original lighting and geometry. Extensive experiments demonstrate that our method outperforms state-of-the-art baselines in both control accuracy and texture fidelity. The code and dataset are publicly accessible at https://github.com/junrongh/ControlTile.
World models based on video generation demonstrate remarkable potential for simulating interactive environments yet suffer from persistent difficulties in two key areas: maintaining long-term content consistency when scenes are revisited and enabling precise camera control from user-specified inputs. Existing methods based on explicit 3D reconstruction often compromise flexibility in unbounded scenarios and struggle to preserve fine-grained structures. Alternative methods rely directly on previously generated frames without establishing explicit spatial correspondence, thereby limiting controllability and consistency. To address these limitations, we present UCM, a novel framework for unified modeling of long-term memory and precise camera control via a time-aware positional encoding warping mechanism. To reduce computational overhead, we design an efficient dual-stream diffusion transformer for high-fidelity generation. Moreover, we introduce a scalable data curation strategy that utilizes point-cloud-based rendering to simulate scene revisiting, enabling training on over 500K monocular videos. Extensive experiments on real-world and synthetic benchmarks demonstrate that UCM significantly outperforms state-of-the-art methods on long-term scene consistency, while achieving precise camera controllability in high-fidelity video generation. Our code is released at https://humanaigc.github.io/ucm-webpage/.
For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamentally limited in realism and large-scale coherence. Conversely, diffusion models offer unprecedented fidelity but remain generally confined to bounded canvases. We introduce InfiniteDiffusion, a training-free algorithm that reformulates diffusion sampling for lazy and unbounded generation, bridging the fidelity of diffusion models with the properties that made procedural noise indispensable: seamless infinite extent, seed-consistency, and constant-time random access. To demonstrate the utility of this approach, we present Terrain Diffusion, a framework for learned procedural terrain generation with a procedural noise-like interface. Our framework outpaces orbital velocity by 9 times on a consumer GPU, enabling realistic terrain generation at interactive rates. We integrate a hierarchical stack of diffusion models to couple planetary context with local detail, a compact Laplacian encoding to stabilize outputs across Earth-scale dynamic ranges, and an open-source infinite-tensor framework for constant-memory manipulation of unbounded tensors. Together, these components position diffusion models as a practical foundation for the next generation of infinite virtual worlds.
Existing texture generation methods rely heavily on latent diffusion models, whose VAE-based spatial compression inherently limits fine-grained detail preservation and degrades pixel-level multi-view consistency. To address this limitation, we introduce PixTex, the first pixel-space multi-view diffusion framework for texture generation, which achieves substantially improved multi-view consistency. Operating directly in image space avoids latent compression, reduces inconsistencies introduced during latent-to-RGB upsampling, and preserves lossless pixel-level geometric guidance for accurate multi-view consistency. However, directly applying pixel-wise attention across multiple views is computationally prohibitive. To balance efficiency and fidelity, we adopt a coarse-to-fine consistency strategy: i) At a coarse patch level, we establish cross-view structural correspondence by employing 5D RoPE to correlate 2D patch coordinates with 3D world-space positions. ii) At the pixel level, a specialized 3D position-aware detailer further refines textural details based on patch features, ensuring fine-grained alignment unattainable by VAE-based methods. Additionally, we propose a novel consistency loss to explicitly guarantee multi-view coherence. Finally, we incorporate a pixel-space multi-view inpainting module to resolve self-occlusions and improve texture completeness. Extensive experiments demonstrate that our framework achieves state-of-the-art multi-view consistency, producing high-fidelity and seamless textures.
3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control relies on screen-space positional gradients, which do not distinguish between geometric misplacement and frequency aliasing, often leading to either over-blurred high-frequency textures or inefficient over-densification. We present a structure-aware densification framework. Our key insight is that the decision to subdivide a Gaussian should be driven by an explicit comparison between its projected screen-space extent and the local structure of the texture it seeks to represent. We introduce a multi-scale frequency analysis combining structure tensors with Laplacian scale space analysis to estimate the dominant frequency at each pixel, enabling robust supervision across varying texture scales. Based on this analysis, we define η , a per-Gaussian, per-axis frequency violation metric that indicates when a primitive may be under-resolving local texture details. Unlike methods that perform isotropic splitting (e.g., splitting each Gaussian into two smaller ones with uniform shape), our approach performs anisotropic splitting. For each axis with high η , we compute a split factor to better resolve the local frequency content. We further introduce a multiview consistency criterion that aggregates η observations across multiple views. By performing densification early and faster, we skip the lengthy iterative densification phases required by baseline methods and achieve significantly faster convergence. Experiments on standard benchmarks demonstrate that our method also achieves superior reconstruction quality, particularly in high-frequency regions.
Inverse sculpting modeling aims to decompose a sculpted mesh into an underlying base shape and reusable geometric details, enabling non-expert users to inherit professional sculpting effort. We present InvSculpt, a novel inverse sculpting framework that decomposes a sculpted mesh into a high-fidelity underlying shape and reusable geometric details represented as a vector displacement field (VDF). Our approach combines semantic priors from text-guided 2D image editing with a 3D rectified flow model to perform inversion-based, mask-free detail removal, recovering an underlying shape that preserves the identity of the source mesh. To represent sculpted details in a lossless and transferable manner, we extract a VDF defined on the surface of the recovered underlying shape and learn a continuous neural representation for geometry-aware transfer. We observe that standard conditional sampling after inversion often suffers from trajectory drift, leading to identity shift and low-frequency distortion. To address this issue, we introduce a trajectory correction strategy that constrains early sampling steps to follow the inversion path, effectively stabilizing subsequent conditional guidance. This design enables robust detail removal and precise extraction of the VDF. Extensive experiments demonstrate that InvSculpt achieves significantly higher-quality mesh decomposition than prior methods and supports a wide range of applications, including geometry redesign and high-fidelity geometric detail transfer.
The eigendecomposition of the Laplace–Beltrami Operator (LBO) is fundamental to geometric analysis, yet computing its low-frequency eigenmodes remains a significant bottleneck due to the high cost of iterative solvers on large-scale data. To amortize this cost, we introduce the Neural Eigenspace Operator (NEO), a feed-forward framework designed to predict the spectrum directly from point clouds. Crucially, NEO circumvents the ill-posed nature of standard eigenvector regression, which suffers from intrinsic sign flips and rotation ambiguities, by learning the stable, invariant low-frequency subspace instead. Specifically, the network predicts a redundant set of basis functions whose span robustly covers the target eigenspace, allowing for the recovery of accurate eigenpairs via a lightweight Rayleigh–Ritz refinement. To handle irregular sampling, we propose a mass-aware neural operator that incorporates per-point area weights into attention-based aggregation, improving robustness to non-uniform densities and enabling zero-shot generalization across resolutions. Our approach achieves near-linear runtime scaling and substantial wall-clock speedups over iterative solvers at comparable accuracy, and exhibits strong zero-shot transfer to high-resolution point clouds. The resulting eigenpairs support standard spectral geometry tasks, while the raw basis functions provide effective point-wise features for downstream learning. Code: https://github.com/Adversarr/NEO.
Despite recent progress, text-to-image models still struggle to generate semantically diverse and compositionally accurate multi-person interaction scenes, often collapsing to repetitive layouts, stereotypical poses, and poorly grounded interactions. In this work, we bridge this gap by introducing a dual pose–image representation that brings person-centric structural priors into pretrained diffusion transformers. Our model jointly predicts a 2D pose visualization image and its corresponding RGB image, enabling structure and appearance to co-evolve during learning. At its core, a cross-modal alignment scheme binds text, pose, and image representations, ensuring consistent grounding across modalities. Furthermore, we design an iterative scene construction scheme, progressively generating complex multi-human interactions while effectively decomposing the overall generation complexity. Extensive experiments demonstrate that our method substantially improves prompt alignment and scene diversity in multi-person image generation.
Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health. We crowdsource large-scale users' posts from six major social media platforms to examine how people discuss their interactions with LLM chatbots across different mental health conditions. Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. Our results reveal that the use of LLM chatbots is condition-specific. Users with neurodivergent conditions (e.g., ADHD, ASD) report strong positive sentiments and instrumental or appraisal support, whereas higher-risk disorders (e.g., schizophrenia, bipolar disorder) show more negative sentiments. We further uncover how user perspectives co-occur with underlying values, such as identity, autonomy, and privacy. Finally, we discuss shifting from ''one-size-fits-all'' chatbot design toward condition-specific, value-sensitive LLM design.
Accurate rainfall forecasting is essential for climate and disaster management, but precipitation exhibits extreme zero inflation that modern time-series Foundation Models (TSFMs) fundamentally cannot represent due to their continuous regression outputs. This structural mismatch causes pervasive drizzle-like false alarms, miscalibrated nonzero intensities, and severely underdetected extremes, while retraining large TSFMs is computationally prohibitive and environmentally unsustainable for most regions. We present a training-free wrapper that corrects zero inflation for frozen TSFMs without updating any parameters. Our method restores discrete zero mass using empirical occurrence statistics, aligns positive-value distributions via probability-integral transforms, and applies Generalized Pareto tail mapping for extreme-value consistency. Experiments on South Australian rainfall show substantial gains with negligible overhead (<5,ms per forecast, compared to hundreds of GPU-hours for retraining). The proposed wrapper enables carbon-neutral, globally deployable climate services and directly advances the goals of UN SDG~13 (Climate Action).