Gaussian Processes

We introduce pixelSplat a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fast 3D reconstruction at inference time. To overcome local minima inherent to sparse and locally supported representations we predict a dense probability distribution over 3D and sample Gaussian means from that probability distribution. We make this sampling operation differentiable via a reparameterization trick allowing us to back-propagate gradients through the Gaussian splatting representation. We benchmark our method on wide-baseline novel view synthesis on the real-world RealEstate10k and ACID datasets where we outperform state-of-the-art light field transformers and accelerate rendering by 2.5 orders of magnitude while reconstructing an interpretable and editable 3D radiance field. Additional materials can be found on the anonymous project website (pixelsplat.github.io).

Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress these techniques often face limitations due to slow optimization or rendering processes leading to extensive training and optimization times. In this paper we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks namely a point decoder and a triplane decoder to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation. This hybrid representation strikes a balance achieving a faster rendering speed compared to implicit representations while simultaneously delivering superior rendering quality than explicit representations. The point decoder is designed for generating point clouds from single images offering an explicit representation which is then utilized by the triplane decoder to query Gaussian features for each point. This design choice addresses the challenges associated with directly regressing explicit 3D Gaussian attributes characterized by their non-structural nature. Subsequently the 3D Gaussians are decoded by an MLP to enable rapid rendering through splatting. Both decoders are built upon a scalable transformer-based architecture and have been efficiently trained on large-scale 3D datasets. The evaluations conducted on both synthetic datasets and real-world images demonstrate that our method not only achieves higher quality but also ensures a faster runtime in comparison to previous state-of-the-art techniques. Please see our project page at https://zouzx.github.io/TriplaneGaussian/

Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry e.g. the Janus issue since it is hard to explicitly integrate 3D priors into implicit 3D representations. Besides it is usually time-consuming for them to generate elaborate 3D models with rich colors. In response this paper proposes GSGEN a novel method that adopts Gaussian Splatting a recent state-of-the-art representation to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting that enables the incorporation of 3D prior. Specifically our method adopts a progressive optimization strategy which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization a coarse representation is established under 3D point cloud diffusion prior along with the ordinary 2D SDS optimization ensuring a sensible and 3D-consistent rough shape. Subsequently the obtained Gaussians undergo an iterative appearance refinement to enrich texture details. In this stage we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs our approach can generate 3D assets with delicate details and accurate geometry. Extensive evaluations demonstrate the effectiveness of our method especially for capturing high-frequency components.

We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D Gaussians as these Gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the Gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction which is fast scalable and preserves details in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally we introduce an optional refinement strategy that binds Gaussians to the surface of the mesh and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing sculpting animating and relighting of the Gaussians by manipulating the mesh instead of the Gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method compared to hours with the state-of-the-art method on SDFs while providing a better rendering quality.

Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However existing approaches still struggle with the view-dependent appearance of glossy surfaces especially under complex lighting of indoor environments. Unlike existing methods which typically assume distant lighting like an environment map we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields which helps decompose appearance into more physically meaningful components.

Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians respectively. Our key idea is to use sparse control points significantly fewer in number than the Gaussians to learn compact 6 DoF transformation bases which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point which reduces learning complexities enhances learning abilities and facilitates obtaining temporal and spatial coherent motion patterns. Then we jointly learn the 3D Gaussians the canonical space locations of control points and the deformation MLP to reconstruct the appearance geometry and dynamics of 3D scenes. During learning the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally thanks to the explicit sparse motion representation and its decomposition from appearance our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications.

Deep neural networks for learning Symmetric Positive Definite (SPD) matrices are gaining increasing attention in machine learning. Despite the significant progress most existing SPD networks use traditional Euclidean classifiers on an approximated space rather than intrinsic classifiers that accurately capture the geometry of SPD manifolds. Inspired by Hyperbolic Neural Networks (HNNs) we propose Riemannian Multinomial Logistics Regression (RMLR) for the classification layers in SPD networks. We introduce a unified framework for building Riemannian classifiers under the metrics pulled back from the Euclidean space and showcase our framework under the parameterized Log-Euclidean Metric (LEM) and Log-Cholesky Metric (LCM). Besides our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks. The effectiveness of our method is demonstrated in three applications: radar recognition human action recognition and electroencephalography (EEG) classification. The code is available at https://github.com/GitZH-Chen/SPDMLR.git.

The fidelity of relighting is bounded by both geometry and appearance representations. For geometry both mesh and volumetric approaches have difficulty modeling intricate structures like 3D hair geometry. For appearance existing relighting models are limited in fidelity and often too slow to render in real-time with high-resolution continuous environments. In this work we present Relightable Gaussian Codec Avatars a method to build high-fidelity relightable head avatars that can be animated to generate novel expressions. Our geometry model based on 3D Gaussians can capture 3D-consistent sub-millimeter details such as hair strands and pores on dynamic face sequences. To support diverse materials of human heads such as the eyes skin and hair in a unified manner we present a novel relightable appearance model based on learnable radiance transfer. Together with global illumination-aware spherical harmonics for the diffuse components we achieve real-time relighting with all-frequency reflections using spherical Gaussians. This appearance model can be efficiently relit under both point light and continuous illumination. We further improve the fidelity of eye reflections and enable explicit gaze control by introducing relightable explicit eye models. Our method outperforms existing approaches without compromising real-time performance. We also demonstrate real-time relighting of avatars on a tethered consumer VR headset showcasing the efficiency and fidelity of our avatars.

3D Gaussians have recently emerged as a highly efficient representation for 3D reconstruction and rendering. Despite its high rendering quality and speed at high resolutions they both deteriorate drastically when rendered at lower resolutions or from far away camera position. During low resolution or far away rendering the pixel size of the image can fall below the Nyquist frequency compared to the screen size of each splatted 3D Gaussian and leads to aliasing effect. The rendering is also drastically slowed down by the sequential alpha blending of more splatted Gaussians per pixel. To address these issues we propose a multi-scale 3D Gaussian splatting algorithm which maintains Gaussians at different scales to represent the same scene. Higher-resolution images are rendered with more small Gaussians and lower-resolution images are rendered with fewer larger Gaussians. With similar training time our algorithm can achieve 13%-66% PSNR and 160%-2400% rendering speed improvement at 4x-128x scale rendering on Mip-NeRF360 dataset compared to the single scale 3D Gaussian splatting.

Recently 3D Gaussian Splatting has demonstrated impressive novel view synthesis results reaching high fidelity and efficiency. However strong artifacts can be observed when changing the sampling rate e.g. by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem we introduce a 3D smoothing filter to constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views. It eliminates high-frequency artifacts when zooming in. Moreover replacing 2D dilation with a 2D Mip filter which simulates a 2D box filter effectively mitigates aliasing and dilation issues. Our evaluation including scenarios such a training on single-scale images and testing on multiple scales validates the effectiveness of our approach.

Humans live in a 3D world and commonly use natural language to interact with a 3D scene. Modeling a 3D language field to support open-ended language queries in 3D has gained increasing attention recently. This paper introduces LangSplat which constructs a 3D language field that enables precise and efficient open-vocabulary querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings in a NeRF model LangSplat advances the field by utilizing a collection of 3D Gaussians each encoding language features distilled from CLIP to represent the language field. By employing a tile-based splatting technique for rendering language features we circumvent the costly rendering process inherent in NeRF. Instead of directly learning CLIP embeddings LangSplat first trains a scene-wise language autoencoder and then learns language features on the scene-specific latent space thereby alleviating substantial memory demands imposed by explicit modeling. Existing methods struggle with imprecise and vague 3D language fields which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features. Extensive experimental results show that LangSplat significantly outperforms the previous state-of-the-art method LERF by a large margin. Notably LangSplat is extremely efficient achieving a 199 x speedup compared to LERF at the resolution of 1440 x 1080. We strongly recommend readers to check out our video results at https://langsplat.github.io/.

Aiming to enhance the utilization of metric space by the parametric softmax classifier recent studies suggest replacing it with a non-parametric alternative. Although a non-parametric classifier may provide better metric space utilization it introduces the challenge of capturing inter-class relationships. A shared characteristic among prior non-parametric classifiers is the static assignment of labels to prototypes during the training i.e. each prototype consistently represents a class throughout the training course. Orthogonal to previous works we present a simple yet effective method to optimize the category assigned to each prototype (label-to-prototype assignment) during the training. To this aim we formalize the problem as a two-step optimization objective over network parameters and label-to-prototype assignment mapping. We solve this optimization using a sequential combination of gradient descent and Bipartide matching. We demonstrate the benefits of the proposed approach by conducting experiments on balanced and long-tail classification problems using different backbone network architectures. In particular our method outperforms its competitors by 1.22% accuracy on CIFAR-100 and 2.15% on ImageNet-200 using a metric space dimension half of the size of its competitors. \href https://github.com/msed-Ebrahimi/DL2PA_CVPR24 Code

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper we present HiFi4G an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3D Gaussian representation with non-rigid tracking achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then we utilize a 4D Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach which significantly outperforms existing approaches in terms of optimization speed rendering quality and storage overhead.

Recent advances in neural rendering have improved both training and rendering times by orders of magnitude. While these methods demonstrate state-of-the-art quality and speed they are designed for photogrammetry of static scenes and do not generalize well to freely moving humans in the environment. In this work we introduce Human Gaussian Splats (HUGS) that represents an animatable human together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only a monocular video with a small number of (50-100) frames and it automatically learns to disentangle the static scene and a fully animatable human avatar within 30 minutes. We utilize the SMPL body model to initialize the human Gaussians. To capture details that are not modeled by SMPL (e.g cloth hairs) we allow the 3D Gaussians to deviate from the human body model. Utilizing 3D Gaussians for animated humans brings new challenges including the artifacts created when articulating the Gaussians. We propose to jointly optimize the linear blend skinning weights to coordinate the movements of individual Gaussians during animation. Our approach enables novel-pose synthesis of human and novel view synthesis of both the human and the scene. We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being 100x faster to train over previous work.

3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods which rely on representations like meshes and point clouds often fall short in realistically depicting complex scenes. On the other hand methods based on implicit 3D representations like Neural Radiance Field (NeRF) render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges our paper presents GaussianEditor the first 3D editing algorithm based on Gaussian Splatting (GS) a novel 3D representation. GaussianEditor enhances precision and control in editing through our proposed Gaussian semantic tracing which traces the editing target throughout the training process. Additionally we propose Hierarchical Gaussian splatting (HGS) to achieve stabilized and fine results under stochastic generative guidance from 2D diffusion models. We also develop editing strategies for efficient object removal and integration a challenging task for existing methods. Our comprehensive experiments demonstrate GaussianEditor's superior control effective and efficient performance marking a significant advancement in 3D editing.

We introduce Gaussian-Flow a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and rendering speeds our approach harnesses recent advancements in point-based 3D Gaussian Splatting (3DGS). Specifically a novel Dual-Domain Deformation Model (DDDM) is proposed to explicitly model attribute deformations of each Gaussian point where the time-dependent residual of each attribute is captured by a polynomial fitting in the time domain and a Fourier series fitting in the frequency domain. The proposed DDDM is capable of modeling complex scene deformations across long video footage eliminating the need for training separate 3DGS for each frame or introducing an additional implicit neural field to model 3D dynamics. Moreover the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene which is comparable to the original 3DGS designed for static 3D reconstruction. Our proposed approach showcases a substantial efficiency improvement achieving a 5xfaster training speed compared to the per-frame 3DGS modeling. In addition quantitative results demonstrate that the proposed Gaussian-Flow significantly outperforms previous leading methods in novel view rendering quality.

We present the first application of 3D Gaussian Splatting in monocular SLAM the most fundamental but the hardest setup for Visual SLAM. Our method which runs live at 3fps utilises Gaussians as the only 3D representation unifying the required representation for accurate efficient tracking mapping and high-quality rendering. Designed for challenging monocular settings our approach is seamlessly extendable to RGB-D SLAM when an external depth sensor is available. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera. First to move beyond the original 3DGS algorithm which requires accurate poses from an offline Structure from Motion (SfM) system we formulate camera tracking for 3DGS using direct optimisation against the 3D Gaussians and show that this enables fast and robust tracking with a wide basin of convergence. Second by utilising the explicit nature of the Gaussians we introduce geometric verification and regularisation to handle the ambiguities occurring in incremental 3D dense reconstruction. Finally we introduce a full SLAM system which not only achieves state-of-the-art results in novel view synthesis and trajectory estimation but also reconstruction of tiny and even transparent objects.

Neural character models can now reconstruct detailed geometry and texture from video but they lack explicit shadows and shading leading to artifacts when generating novel views and poses or during relighting. It is particularly difficult to include shadows as they are a global effect and the required casting of secondary rays is costly. We propose a new shadow model using a Gaussian density proxy that replaces sampling with a simple analytic formula. It supports dynamic motion and is tailored for shadow computation thereby avoiding the affine projection approximation and sorting required by the closely related Gaussian splatting. Combined with a deferred neural rendering model our Gaussian shadows enable Lambertian shading and shadow casting with minimal overhead. We demonstrate improved reconstructions with better separation of albedo shading and shadows in challenging outdoor scenes with direct sun light and hard shadows. Our method is able to optimize the light direction without any input from the user. As a result novel poses have fewer shadow artifacts and relighting in novel scenes is more realistic compared to the state-of-the-art methods providing new ways to pose neural characters in novel environments increasing their applicability. Code available at: https://github.com/LuisBolanos17/GaussianShadowCasting

Creating high-fidelity 3D head avatars has always been a research hotspot but there remains a great challenge under lightweight sparse view setups. In this paper we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions. Project page: https://yuelangx.github.io/gaussianheadavatar.