Creating animatable 3D avatars from a single image remains challenging due to style limitations (realistic, cartoon, anime) and difficulties in handling accessories or hairstyles. While 3D diffusion models advance single-view reconstruction for general objects, outputs often lack animation controls or suffer from artifacts because of the domain gap. We propose SOAP, a style-omniscient framework to generate rigged, topology-consistent avatars from any portrait. Our method leverages a multiview diffusion model trained on 24K 3D heads with multiple styles and an adaptive optimization pipeline to deform the FLAME mesh while maintaining topology and rigging via differentiable rendering. The resulting textured avatars support FACS-based animation, integrate with eyeballs and teeth, and preserve details like braided hair or accessories. Extensive experiments demonstrate the superiority of our method over state-of-the-art techniques for both single-view head modeling and diffusion-based generation of Image-to-3D. Our code and data are publicly available for research purposes at github.com/TingtingLiao/soap.
论文检索
输入标题、作者或关键词，从 1,093 篇学术成果中精准定位
We leverage repetitive elements in 3D scenes to improve novel view synthesis. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly improved novel view synthesis but renderings of unseen and occluded parts remain low-quality if the training views are not exhaustive enough. Our key observation is that our environment is often full of repetitive elements. We propose to leverage those repetitions to improve the reconstruction of low-quality parts of the scene due to poor coverage and occlusions. We propose a method that segments each repeated instance in a 3DGS reconstruction, registers them together, and allows information to be shared among instances. Our method improves the geometry while also accounting for appearance variations across instances. We demonstrate our method on a variety of synthetic and real scenes with typical repetitive elements, leading to a substantial improvement in the quality of novel view synthesis.
GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering
PDF ↗Video stabilization is pivotal for video processing, as it removes unwanted shakiness while preserving the original user motion intent. Existing approaches, depending on the domain they operate, suffer from several issues (e.g. geometric distortions, excessive cropping, poor generalization) that degrade the user experience. To address these issues, we introduce GaVS, a novel 3D-grounded approach that reformulates video stabilization as a temporally-consistent ‘local reconstruction and rendering’ paradigm. Given 3D camera poses, we augment a reconstruction model to predict Gaussian Splatting primitives, and finetune it at test-time, with multi-view dynamics-aware photometric supervision and cross-frame regularization, to produce temporally-consistent local reconstructions. The model are then used to render each stabilized frame. We utilize a scene extrapolation module to avoid frame cropping. Our method is evaluated on a repurposed dataset, instilled with 3D-grounded information, covering samples with diverse camera motions and scene dynamics. Quantitatively, our method is competitive with or superior to state-of-the-art 2D and 2.5D approaches in terms of conventional task metrics and new geometry consistency. Qualitatively, our method produces noticeably better results compared to alternatives, validated by the user study. Project Page: sinoyou.github.io/gavs.
In this paper, we highlight a critical yet often overlooked factor in most 3D human tasks, namely modeling complicated 3D human with with hand-held objects or loose-fitting clothing. It is known that the parameterized formulation of SMPL is able to fit human skin; while hand-held objects and loose-fitting clothing, are difficult to get modeled within the unified framework, since their movements are usually decoupled with the human body.To enhance the capability of SMPL skeleton in response to this situation, we propose a growth strategy that enables the joint tree of the skeleton to expand adaptively. Specifically, our method, called ToMiE, consists of parent joints localization and external joints optimization. For parent joints localization, we employ a gradient-based approach guided by both LBS blending weights and motion kernels.Once the external joints are obtained, we proceed to optimize their transformations in SE(3) across different frames, enabling rendering and explicit animation.ToMiE manages to outperform other methods across various cases with hand-held objects and loose-fitting clothing, not only in rendering quality but also by offering free animation of grown joints, thereby enhancing the expressive ability of SMPL skeleton for a broader range of applications.
GSRecon: Efficient Generalizable Gaussian Splatting for Surface Reconstruction from Sparse Views
PDF ↗Generalizable surface reconstruction aims to recover the surface the scene from a sparse set of images in a feed-forward manner. Existing volume rendering-based methods evaluate numerous points along camera rays to infer the geometry, resulting in inefficient reconstruction. Recently, 3D Gaussian Splatting offers an alternative efficient scene representation and has inspired a series of surface reconstruction methods. However, these methods require dense views and cannot be generalized to new scenes. In this paper, we propose a novel surface reconstruction method with Gaussian splatting, named GSRecon, which leverages the advantages of rasterization-based rendering to achieve efficient reconstruction. To obtain accurate geometry representation, we propose a geometry-aware cross-view enhancement module to improve the unreliable geometry estimation in the current view by incorporating accurate geometric information from other views. To generate the fine-grained Gaussian primitives, we propose a hybrid cross-view feature aggregation module that integrates an efficient voxel branch and a fine-grained point branch to jointly capture cross-view geometric information. Subsequently, per-view depth maps are rendered using these Gaussian primitives and fused to obtain the final 3D surface. Extensive experiments on the DTU, BlendedMVS, and Tanks and Temples datasets validate that GSRecon achieves state-of-the-art performance efficiently. Code is available at https://github.com/hyangwinter/GSRecon.
Reconstructing 3D scenes from a single image is a fundamentally ill-posed task due to the severely under-constrained nature of the problem. Consequently, when the scene is rendered from novel camera views, particularly in unseen regions far away from the input camera, existing single image to 3D reconstruction methods render incoherent and blurry views. In this work, we address these inherent limitations in existing single image-to-3D scene feedforward networks. To alleviate the poor performance due to insufficient information beyond the input image's view, we leverage a strong generative prior in the form of a pre-trained latent video diffusion model, for iterative refinement of a coarse scene represented by optimizable Gaussian parameters. To ensure that the style and texture of the generated images align with that of the input image, we incorporate on-the-fly Fourier-style transfer between the generated images and the input image. Additionally, we design a semantic uncertainty quantification module which calculates the per-pixel entropy and yields uncertainty maps which are used to guide the refinement process from the most confident pixels while discarding the remaining highly uncertain ones. We conduct extensive experiments on real-world scene datasets, including in-domain RealEstate-10K and out-of-domain KITTI-v2, showing that our approach can provide more realistic and high-fidelity novel view synthesis results compared to existing state-of-the-art methods.
SparseRecon: Neural Implicit Surface Reconstruction from Sparse Views with Feature and Depth Consistencies
PDF ↗Surface reconstruction from sparse views aims to reconstruct a 3D shape or scene from few RGB images. The latest methods are either generalization-based or overfitting-based. However, the generalization-based methods do not generalize well on views that were unseen during training, while the reconstruction quality of overfitting-based methods is still limited by the limited geometry clues. To address this issue, we propose SparseRecon, a novel neural implicit reconstruction method for sparse views with volume rendering-based feature consistency and uncertainty-guided depth constraint. Firstly, we introduce a feature consistency loss across views to constrain the neural implicit field. This design alleviates the ambiguity caused by insufficient consistency information of views and ensures completeness and smoothness in the reconstruction results. Secondly, we employ an uncertainty-guided depth constraint to back up the feature consistency loss in areas with occlusion and insignificant features, which recovers geometry details for better reconstruction quality. Experimental results demonstrate that our method outperforms the state-of-the-art methods, which can produce high-quality geometry with sparse-view input, especially in the scenarios with small overlapping views. Project page: https://hanl2010.github.io/SparseRecon/.
Surface reconstruction is fundamental to computer vision and graphics, enabling applications in 3D modeling, mixed reality, robotics, and more. Existing approaches based on volumetric rendering obtain promising results, but optimize on a per-scene basis, resulting in a slow optimization that can struggle to model under-observed or textureless regions. We introduce QuickSplat, which learns data-driven priors to generate dense initializations for 2D gaussian splatting optimization of large-scale indoor scenes. This provides a strong starting point for the reconstruction, which accelerates the convergence of the optimization and improves the geometry of flat wall structures. We further learn to jointly estimate the densification and update of the scene parameters during each iteration; our proposed densifier network predicts new Gaussians based on the rendering gradients of existing ones, removing the needs of heuristics for densification. Extensive experiments on large-scale indoor scene reconstruction demonstrate the superiority of our data-driven optimization. Concretely, we accelerate runtime by 8x, while decreasing depth errors by 48% in comparison to state of the art methods.
A concept of light-fields computed from multiple view images on regular grids has proven its benefit for scene representations, and supported realistic renderings of novel views and photographic effects such as refocusing and shallow depth of field. In spite of its effectiveness of light flow computations, obtaining light fields requires either computational costs or specialized devices like a bulky camera setup and a specialized microlens array. In an effort to broaden its benefit and applicability, in this paper, we propose a novel view synthesis method for light field generation from only single images, named inverse image-based rendering. Unlike previous attempts to implicitly rebuild 3D geometry or to explicitly represent objective scenes, our method reconstructs light flows in a space from image pixels, which behaves in the opposite way to image-based rendering. To accomplish this, we design a neural rendering pipeline to render a target ray in an arbitrary viewpoint. Our neural renderer first stores the light flow of source rays from the input image, then computes the relationships among them through cross-attention, and finally predicts the color of the target ray based on these relationships. After the rendering pipeline generates the first novel view from a single input image, the generated out-of-view contents are updated to the set of source rays, and this procedure is iteratively performed while ensuring the consistent generation of occluded contents. We demonstrate that our inverse image-based rendering works well with various challenging datasets without any retraining or finetuning after once trained on synthetic dataset. In addition, our method outperforms relevant state-of-the-art novel view synthesis methods.
We propose Long-LRM, a feed-forward 3D Gaussian reconstruction model for instant, high-resolution, 360deg wide-coverage, scene-level reconstruction. Specifically, it takes in 32 input images at a resolution of 960x540 and produces the Gaussian reconstruction in just 1 second on a single A100 GPU. To handle the long sequence of 250K tokens brought by the large input size, Long-LRM features a mixture of the recent Mamba2 blocks and the classical transformer blocks, enhanced by a light-weight token merging module and Gaussian pruning steps that balance between quality and efficiency. We evaluate Long-LRM on the large-scale DL3DV benchmark and Tanks&Temples, demonstrating reconstruction quality comparable to the optimization-based methods while achieving an 800x speedup w.r.t. the optimization-based approaches and an input size at least 60x larger than the previous feed-forward approaches. We conduct extensive ablation studies on our model design choices for both rendering quality and computation efficiency. We also explore Long-LRM's compatibility with other Gaussian variants such as 2D GS, which enhances Long-LRM's ability in geometry reconstruction. Project page: http://arthurhero.github.io/projects/llrm/
Creating high-fidelity 3D meshes with arbitrary topology, including open surfaces and complex interiors, remains a significant challenge. Existing implicit field methods often require costly and detail-degrading watertight conversion, while other approaches struggle with high resolutions. This paper introduces SparseFlex, a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to 1024^3 directly from rendering losses. SparseFlex combines the accuracy of Flexicubes with a sparse voxel structure, focusing computation on surface-adjacent regions and efficiently handling open surfaces. Crucially, we introduce a frustum-aware sectional voxel training strategy that activates only relevant voxels during rendering, dramatically reducing memory consumption and enabling high-resolution training. This also allows, for the first time, the reconstruction of mesh interiors using only rendering supervision. Building upon this, we demonstrate a complete shape modeling pipeline by training a variational autoencoder (VAE) and a rectified flow transformer for high-quality 3D shape generation. Our experiments show state-of-the-art reconstruction accuracy, with a 82% reduction in Chamfer Distance and a 88% increase in F-score compared to previous methods, and demonstrate the generation of high-resolution, detailed 3D shapes with arbitrary topology. By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability limitations (requiring 3D supervision or costly annotations), robustness issues (being susceptible to local optima), and rendering shortcomings (lacking speed or photorealism). We introduce SplArt, a self-supervised, category-agnostic framework that uses 3D Gaussian Splatting (3DGS) to reconstruct and infer the kinematics of articulated objects from two sets of posed RGB images captured at different articulation states, enabling real-time photorealistic rendering for novel viewpoints and articulations. SplArt augments 3DGS with a differentiable mobility parameter per Gaussian, achieving refined part segmentation. A multi-stage optimization strategy is employed to progressively handle reconstruction, part segmentation, and articulation estimation, significantly enhancing robustness and accuracy. SplArt exploits geometric self-supervision, effectively addressing challenging scenarios without requiring 3D annotations or category-specific priors. Evaluations on established and newly proposed benchmarks, along with applications to real-world scenarios using a handheld RGB camera, demonstrate SplArt's state-of-the-art performance and real-world practicality. Code is publicly available at https://github.com/ripl/splart.
ArchiSet: Benchmarking Editable and Consistent Single-View 3D Reconstruction of Buildings with Specific Window-to-Wall Ratios
PDF ↗Image-based 3D Genetation has made significant progress in typical scenarios, achieving high fidelity in capturing intricate textures. However, in the Architecture, Engineering, and Construction (AEC) design stages, existing technologies still face considerable challenges, particularly in handling specific window-to-wall ratios, ensuring window detail consistency, and enabling interactive editing. To address this research gap and encourage greater community attention on this practical architectural design problem, we propose a new task: Editable and Consistent Single-View 3D Genetation of Buildings with Specific Window-to-Wall Ratios. To accomplish this: 1) We introduce the ArchiSet dataset, the first public, real-world architectural design dataset, including 13,728 3D building forms in the format of point clouds, voxels, meshes, and window-to-wall ratio information, providing comprehensive support for 3D architectural design research. The dataset also contains over 1,482,624 images in three types--sketches, color block diagrams, and renderings--accompanied by paired window masks for detailed evaluation. 2) We evaluated state-of-the-art single-view 3D Genetation algorithms on ArchiSet, identifying several limitations, such as the loss of volumetric detail, incomplete window details, and limited editability. 3) We introduce BuildingMesh, a diffusion model specifically designed for generating and editing 3D architectural forms from a single image with customizable window-to-wall ratios, suitable for dynamic architectural design workflows. We propose an regularized method to ensure window consistency. Our framework also includes an interactive module for easy further editing, enhancing platform efficiency and accuracy in professional architectural design workflows. Experimental results demonstrate that BuildingMesh achieves high-quality 3D generation with improved design flexibility.
3D Gaussian Splatting (3DGS) has gained significant attention for their high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction. In this study, we propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed. Considering the complex motion patterns inherent in real-world camera movements, we predict continuous camera trajectories using neural ordinary differential equations (ODEs). To ensure accurate modeling, we employ rigid body transformations, preserving the shape and size of the object but rely on the discrete integration of sampled frames. To better approximate the continuous nature of motion blur, we introduce a continuous motion refinement (CMR) transformation that refines rigid transformations by incorporating additional learnable parameters. By revisiting fundamental camera theory and leveraging advanced neural ODE techniques, we achieve precise modeling of continuous camera trajectories, leading to improved reconstruction accuracy. Extensive experiments demonstrate state-of-the-art performance both quantitatively and qualitatively on benchmark datasets, which include a wide range of motion blur scenarios, from moderate to extreme blur.
Fine-Grained 3D Gaussian Head Avatars Modeling from Static Captures via Joint Reconstruction and Registration
PDF ↗Recently, 3D head avatar modeling based on 3D Gaussians has demonstrated significant advantages in rendering quality and efficiency, given sufficient data. Some efforts have begun to train prior models on large datasets to develop generalizable 3D Gaussian head avatar modeling methods. Unfortunately, due to the limited expressive power of identity-shared 3D representations, prior-based modeling often results in degenerate rendering quality. To overcome this limitation, we propose to formulate the 3D Gaussian head avatar modeling as a joint reconstruction and registration problem. Given static input images (e.g., a short mobile phone capture), we optimize two sets of 3D Gaussians: the prior-based one possesses complete animation rigging information inferred from the prior model and produces plausible modeling results, while the prior-free one is used to more freely capture the fine-grained geometric and texture details in the input images. Additionally, we simultaneously solve the registration problem between the two 3D Gaussian sets. On the one hand, the registration results will provide binding information for the prior-free reconstruction to make it animatable. On the other hand, during optimization, the prior-based Gaussian can regularize the prior-free reconstruction to resist overfitting and perform well in novel expressions. Finally, we merge the parts of the prior-based reconstruction that are occluded in the input images with the prior-free reconstruction set and then apply appropriate post-processing strategies (such as teeth enhancement) to produce a complete head avatar. We evaluated our method on the public Nersemble dataset and our own in-the-wild data. The experiments demonstrate that, under the same experimental settings, our method significantly improves modeling quality and provides better support for detailed modeling at higher resolutions.
We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffusion models have been successfully used to reconstruct 3D objects from a single image efficiently. However, reflectance is still represented by simple material models or needs to be estimated in additional pipeline steps to enable relighting and controlled appearance edits. We extend a latent video diffusion model to output spatially-varying PBR parameters and surface normals jointly with each generated RGB view based on explicit camera control. This unique setup allows for direct relighting in a 2.5D setting, and for generating a 3D asset using our model as neural prior. We introduce various mechanisms to this pipeline that improve quality in this ill-posed setting. We show state-of-the-art relighting and novel view synthesis performance on multiple object-centric datasets. Our method generalizes to diverse image inputs, enabling the generation of relightable 3D assets useful in AR/VR, movies, games and other visual media.
MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction under Various Light Conditions
PDF ↗Novel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3DGS). Despite recent progress, these tasks are often addressed independently, with GS-based rendering methods struggling under diverse light conditions and failing to produce accurate surfaces, while GS-based reconstruction methods frequently compromise rendering quality. This raises a central question: must rendering and reconstruction always involve a trade-off? To address this, we propose MGSR, a 2D/3D Mutual-boosted Gaussian Splatting for Surface Reconstruction that enhances both rendering quality and 3D reconstruction accuracy. MGSR introduces two branches--one based on 2DGS and the other on 3DGS. The 2DGS branch excels in surface reconstruction, providing precise geometry information to the 3DGS branch. Leveraging this geometry, the 3DGS branch employs a geometry-guided illumination decomposition module that captures reflected and transmitted components, enabling realistic rendering under varied light conditions. Using the transmitted component as supervision, the 2DGS branch also achieves high-fidelity surface reconstruction. Throughout the optimization process, the 2DGS and 3DGS branches undergo alternating optimization, providing mutual supervision. Prior to this, each branch completes an independent warm-up phase, with an early stopping strategy implemented to reduce computational costs. We evaluate MGSR on a diverse set of synthetic and real-world datasets, at both object and scene levels, demonstrating strong performance in rendering and surface reconstruction. Code is available at https://github.com/TsingyuanChou/MGSR.
OcRFDet: Object-Centric Radiance Fields for Multi-View 3D Object Detection in Autonomous Driving
PDF ↗Current multi-view 3D object detection methods typically transfer 2D features into 3D space using depth estimation or 3D position encoder, but in a fully data-driven and implicit manner, which limits the detection performance. Inspired by the success of radiance fields on 3D reconstruction, we assume they can be used to enhance the detector's ability of 3D geometry estimation. However, we observe a decline in detection performance, when we directly use them for 3D rendering as an auxiliary task. From our analysis, we find the performance drop is caused by the strong responses on the background when rendering the whole scene. To address this problem, we propose object-centric radiance fields, focusing on modeling foreground objects while discarding background noises. Specifically, we employ object-centric radiance fields (OcRF) to enhance 3D voxel features via an auxiliary task of rendering foreground objects. We further use opacity - the side-product of rendering- to enhance the 2D foreground BEV features via height-aware opacity-based attention (HOA), where attention maps at different height levels are generated separately via multiple networks in parallel. Extensive experiments on the nuScenes validation and test datasets demonstrate that our OcRFDet achieves superior performance, outperforming previous state-of-the-art methods with 57.2% mAP and 64.8% NDS on the nuScenes test benchmark.
CityGS-X: A Scalable Architecture for Efficient and Geometrically Accurate Large-Scale Scene Reconstruction
PDF ↗Despite its significant achievements in large-scale scene reconstruction, 3D Gaussian Splatting still faces substantial challenges, including slow processing, high computational costs, and limited geometric accuracy. These core issues arise from its inherently unstructured design and the absence of efficient parallelization. To overcome these challenges simultaneously, we introduce CityGS-X, a scalable architecture built on a novel parallelized hybrid hierarchical 3D representation (PH2-3D). As an early attempt, CityGS-X abandons the cumbersome merge-and-partition process and instead adopts a newly designed batch-level multi-task rendering process. This architecture enables efficient multi-GPU rendering through dynamic Level-of-Detail voxel allocations, significantly improving scalability and performance. Through extensive experiments, CityGS-X consistently outperforms existing methods in terms of faster training times, larger rendering capacities, and more accurate geometric details in large-scale scenes. To further enhance both overall quality and geometric accuracy, CityGS-X presents a progressive RGB-Depth-Normal training strategy. This approach enhances 3D consistency by jointly optimizing appearance and geometry representation through multi-view constraints and off-the-shelf depth priors within batch-level training. Notably, CityGS-X can train and render a scene with more than 5000 images in just five hours using only four 4090 GPUs, a task that would make other alternative methods encounter out-of-memory issues and fail completely. This implies that CityGS-X is far beyond the capacity of other existing methods.
3D Gaussian Splatting (3DGS) has recently emerged as an efficient representation for high-quality 3D reconstruction and rendering. Despite its superior rendering quality and speed, 3DGS heavily relies on the assumption of geometric consistency among input images. In real-world scenarios, violations of this assumption--such as occlusions, dynamic objects, or camera blur--often lead to reconstruction artifacts and rendering inaccuracies. To address these challenges, we introduce RogSplat, a robust framework that leverages generative models to enhance the reliability of 3DGS. Specifically, RogSplat identifies and rectifies occluded regions during the optimization of unstructured scenes. Outlier regions are first detected using our proposed fused features and then accurately inpainted by the proposed RF-Refiner, ensuring reliable reconstruction of occluded areas while preserving the integrity of visible regions. Extensive experiments demonstrate that RogSplat achieves state-of-the-art reconstruction quality on the RobustNeRF and NeRF-on-the-go datasets, significantly outperforming existing methods in challenging real-world scenarios involving dynamic objects.