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The growing demand for personalized 3D avatars calls for efficient reconstruction methods from casual photos. This task remains challenging due to unconstrained viewpoints, partial body visibility, and temporal variations across input images. While some previous methods circumvent these difficulties by adopting generative approaches like score distillation, they struggle to preserve authentic appearance details from source images. To address these limitations, we introduce Visual-Fusion-Avatar (VFAvatar), a novel feed-forward framework that reconstructs 3D avatars by fusing visual cues in just a few seconds. VFAvatar couples a pose-free reconstruction foundation model with a pretrained human generation prior in a mutually reinforcing manner. And we propose a visibility-aware, view-attentive residual aggregation mechanism that routes and fuses per-view updates, allowing partial observations from different images to be assembled into a single coherent avatar. Experiments demonstrate that VFAvatar significantly outperforms state-of-the-art methods in both reconstruction fidelity and efficiency, while enabling shape and pose manipulation. Code is available on: https://github.com/huangshuo200823/VFAvatar.
Existing volumetric capture of dynamic human performance achieves high fidelity with dense camera arrays. However, in real-world scenarios, only a handful of low-overlap cameras are available, which degrades the output quality and leaves large areas unobserved. Recent 4D reconstruction methods have focused on low-overlap settings, yet they still produce noticeable artifacts in under-observed regions. Video diffusion models have emerged as another option, but they show geometrically inconsistent results for humans. To address these limitations, we propose StudioRecon, a pipeline that reconstructs 4D human scenes from sparse, low-overlap cameras by decoupling background and humans. We densify background supervision by synthesizing hundreds of camera-controlled novel views with a video diffusion model. We also robustly initialize deformable Gaussian humans with cross-view identity association and triangulated multi-view keypoint fitting. Finally, our recursive enhancement module with motion-adaptive consistency injection harmonizes the composed output, thereby further avoiding remaining artifacts. We achieve state-of-the-art novel view synthesis across four real-world datasets and demonstrate applications such as novel trajectory rendering and human replacement. Project page: https://sisyphm.github.io/studiorecon-page/.
With recent advances in VR, 3D sketches have emerged as a powerful medium for 3D model creation. However, while they already provide the user with a good perception of the intended shape, they must be surfaced before any reuse in a downstream application. This remains a challenge when sketches are unoriented, i.e., when they are simply sets of unsorted 3D strokes, without any additional normal information. We introduce NeuralSketch2Surf, the first fast and robust neural surfacing solution that processes arbitrarily unoriented sketches at interactive rates. Our approach uses S2V-Net, a new transformer network designed to mesh 3D sketches. Instead of directly inferring complex functions to represent shapes, we focus on predicting an occupancy grid, then refined using a custom smoothing function to create the desired surface. Thanks to a lightweight architecture that enables fast predictions, our method produces results in less than 2 seconds, in contrast to SOTA techniques that can take minutes or even hours. Extensive evaluations demonstrate that our method is not only fast but also generates closed surfaces with high geometric, topological, and perceptual accuracy.
Signed distance fields (SDFs) and their multi-channel variants (MSDFs) are widely recognized as an effective and practical solution for high-quality, real-time vector graphics rendering. However, generating these fields remains a significant CPU-bound bottleneck, limiting their use in dynamic, interactive applications. This paper introduces a novel, GPU-accelerated algorithm that reframes SDF generation as a parallel quadratic stroke rendering problem. By leveraging the GPU’s tessellation and fragment evaluation pipeline, we parallelize distance calculations for every pixel within a stroke’s influence region. We introduce a localized sign-resolution scheme and a shader-based pipeline that merges per-curve distances using efficient atomic operations. Our results demonstrate a substantial speedup over state-of-the-art CPU libraries like MSDFGen and Skia while maintaining comparable visual accuracy. This performance breakthrough enables real-time generation of distance fields for dynamic user interfaces and on-the-fly synthetic data creation.
Vectorization of line art and sketches is an important problem in computer graphics and remains an active area of research. In this paper, we leverage several recent advances in the field to introduce a new state-of-the-art method for line art vectorization. First, we show that it is possible to go beyond heuristics to extract a meaningful set of strokes from a raster image. We leverage both depth prediction and semantic feature extraction models to propose a more principled approach to split the skeleton graph of the sketch into subgraphs to initialize a set of strokes, producing results that better align with artistic intent. Second, we model strokes as Bézier curves with both geometric and appearance components, and employ 2D Gaussian splatting for fast, differentiable rendering. This formulation enables efficient fitting of strokes to the input while jointly optimizing control points and brush texture. We further demonstrate promising results on video by incorporating temporal tracking and adaptive keyframe introduction. Our evaluation shows that we achieve state-of-the-art results for image reconstruction, while being fast and producing strokes of high quality. More importantly, the fitting is highly compatible with user input, through the correction of the splines and the selection of optimization parameters.
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.
Low-discrepancy points have proven to be a useful source of samples for numerical integration via quasi-Monte Carlo integration. Low-discrepancy points are typically uniformly distributed in the unit hypercube. It is thus common to transform the input uniform points into target non-uniform samples via mapping, just like how uniform random numbers are used for generating non-uniform samples. Unfortunately, this common approach does not guarantee to carry over the low-discrepancy nature of the input points into the output non-uniform samples, giving the resulting non-uniform samples suboptimal discrepancy. The existing methods for generating low-discrepancy points focus only on uniform points, thus generating non-uniform and low-discrepancy samples has been an open problem. We introduce an optimization framework to approximate non-uniform low-discrepancy samples by numerically minimizing the discrepancy metric for non-uniform samples. We propose a differentiable numerical estimator of the generalized non-uniform discrepancy to enable gradient-based optimization. Our differentiable discrepancy estimator turns a challenging problem of generating non-uniform low-discrepancy samples into a simple numerical optimization problem. Since optimization can generate only one set of samples, we further propose a neural network architecture to encode the sample generation process, allowing us to generate multiple sets of samples from conditional targets without runtime optimization. We experimentally validate that our method reduces discrepancy below prior methods at least up to a certain sample size and consequently achieves lower integration error on numerical integration problems.
Normal orientation has been studied extensively, yet reliably orienting normals on scene-scale point clouds remains challenging. Most existing approaches are tailored to watertight surfaces and often degrade on non-watertight scenes. The few methods that do support non-watertight geometry are typically too slow to handle very large inputs. In this paper, we propose a robust and efficient approach for orienting normals on super-large, non-watertight scenes. Our key insight is that, although global shapes vary widely, local geometric structures are often similar. Based on this observation, we adopt a divide-and-conquer strategy that decomposes the input into smaller patches. We then train a self-conditioning neural network to orient normals within each patch, enabling high efficiency and straightforward parallelization. The remaining challenge is to enforce global consistency across patches. To this end, we train a second network to predict pairwise consistency weights for adjacent patches under potential flipping. Using these weights, we cast global patch reconciliation as a 0-1 integer programming with <1000 binary variables, which can be solved efficiently with off-the-shelf solvers. Since both networks operate only on local patches, our method generalizes well across datasets. Experiments on diverse scene-level data demonstrate that our approach is robust and scales to inputs with up to 50 million points. Code and pre-trained models are publicly available at https://github.com/zd-lee/MegaNorm.
Radar neural reconstruction methods have recently achieved robust 3D scene occupancy from radar measurements alone, as they provide metric depth and are insensitive to adverse weather and low light. However, while these methods can recover some 3D geometry, their input radar data mixes information across elevation into a 2D range-azimuth measurement. This fundamentally limits their elevation resolution, especially in automotive scenes with limited vertical baselines. Camera images offer the opposite tradeoff: they contain strong, high resolution cues for object elevation but struggle with accurate depth and in adverse conditions. We propose CaRaFe, a method that leverages the complementary strengths of camera and radar for 3D reconstruction in challenging urban settings. CaRaFe employs a single multi-modal neural field, relying on conventional novel view synthesis as a supervision signal. Radar supervision provides a valuable geometric constraint to camera rendering that reduces shape-radiance ambiguity, while camera supervision allows for more accurate object elevation disambiguation and fewer missing structures in regions weakly observed by radar. We validate CaRaFe across diverse in-the-wild driving scenes, demonstrating favorable reconstruction quality over both radar and camera methods. Code for this paper is available at light.princeton.edu/carafe.
Surface reconstruction from unoriented point clouds is a fundamental problem in geometry processing. While recent winding-number-based approaches, such as Parametric Gauss Reconstruction (PGR) and Anisotropic Gauss Reconstruction (AGR), provide elegant implicit formulations, they often suffer from underdetermined or ill-conditioned linear systems. In this work, we analyze the theoretical root of this instability: the winding number formula is oblivious to a broad family of tangential perturbations of the normal field, which introduces a non-trivial nullspace in the resulting integral equations. To address this, we present G3R, a simple yet effective method based on Green’s third identity for harmonic functions. This approach eliminates the tangential nullspace with a basis of harmonic functions, resulting in a well-conditioned linear system which can be solved directly without additional regularization. Furthermore, we apply a Gaussian mollification of the fundamental solution, which handles near-field singularities robustly. We adopt a matrix-free implementation and accelerate it with a hierarchical scheme to scale our method to large point clouds. Experiments demonstrate that our approach achieves highly competitive performance in both normal orientation accuracy and surface reconstruction quality. Our code is publicly available at https://github.com/ne0-wu/G3R.
Motion in-betweening is one of the most artistically demanding and time-consuming stages of 3D animation, where the expressivity and rhythm of motion are defined. The level of creative control it requires makes it a major production bottleneck, underscoring the need for intelligent tools that assist animators in this process. Although recent deep learning approaches have achieved strong results in motion synthesis and in-betweening, they assume data characteristics, motion styles, and problem formulations that diverge from professional animation workflows. To bridge this gap, we propose a method explicitly aligned with the constraints of motion in-betweening for keyframe-based animation in production environments. At its core, the Adaptive Interpolation–Synthesis (AIS) layer mirrors the animator’s creative process by dynamically balancing learned interpolation and direct pose synthesis. In addition, a domain-based input keypose schedule reflects the distribution of production data, improving stylistic consistency and alignment between training and real-world usage. Our method achieves state-of-the-art performance on production data; when integrated into Autodesk Maya, it enables animators to complete in-betweening tasks with a 3.5 × speedup.