CVPR 2024 论文列表

Social interaction is a fundamental aspect of human behavior and communication. The way individuals position themselves in relation to others also known as proxemics conveys social cues and affects the dynamics of social interaction. Reconstructing such interaction from images presents challenges because of mutual occlusion and the limited availability of large training datasets. To address this we present a novel approach that learns a prior over the 3D proxemics two people in close social interaction and demonstrate its use for single-view 3D reconstruction. We start by creating 3D training data of interacting people using image datasets with contact annotations. We then model the proxemics using a novel denoising diffusion model called BUDDI that learns the joint distribution over the poses of two people in close social interaction. Sampling from our generative proxemics model produces realistic 3D human interactions which we validate through a perceptual study. We use BUDDI in reconstructing two people in close proximity from an image without any contact annotation via an optimization approach that uses the diffusion model as a prior. Our approach recovers accurate 3D social interactions from noisy initial estimates outperforming state-of-the-art methods. Our code data and model are available at: muelea.github.io/buddi.

Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry of individual parts neglecting part-whole hierarchies of objects. Leveraging two key observations: 1) super-part poses provide strong hints about part poses and 2) predicting super-part poses is easier due to fewer super-parts we propose a part-whole-hierarchy message passing network for efficient 3D part assembly. We first introduce super-parts by grouping geometrically similar parts without any semantic labels. Then we employ a part-whole hierarchical encoder wherein a super-part encoder predicts latent super-part poses based on input parts. Subsequently we transform the point cloud using the latent poses feeding it to the part encoder for aggregating super-part information and reasoning about part relationships to predict all part poses. In training only ground-truth part poses are required. During inference the predicted latent poses of super-parts enhance interpretability. Experimental results on the PartNet dataset that our method achieves state-of-the-art performance in part and connectivity accuracy and enables an interpretable hierarchical part assembly.

In this paper we propose the first generalizable view synthesis approach that specifically targets multi-view stereo-camera images. Since recent stereo matching has demonstrated accurate geometry prediction we introduce stereo matching into novel-view synthesis for high-quality geometry reconstruction. To this end this paper proposes a novel framework dubbed StereoNeRF which integrates stereo matching into a NeRF-based generalizable view synthesis approach. StereoNeRF is equipped with three key components to effectively exploit stereo matching in novel-view synthesis: a stereo feature extractor a depth-guided plane-sweeping and a stereo depth loss. Moreover we propose the StereoNVS dataset the first multi-view dataset of stereo-camera images encompassing a wide variety of both real and synthetic scenes. Our experimental results demonstrate that StereoNeRF surpasses previous approaches in generalizable view synthesis.

The pre-training architectures of large language models encompass various types including autoencoding models autoregressive models and encoder-decoder models. We posit that any modality can potentially benefit from a large language model as long as it undergoes vector quantization to become discrete tokens. Inspired by the General Language Model we propose a General Point Model (GPM) that seamlessly integrates autoencoding and autoregressive tasks in a point cloud transformer. This model is versatile allowing fine-tuning for downstream point cloud representation tasks as well as unconditional and conditional generation tasks. GPM enhances masked prediction in autoencoding through various forms of mask padding tasks leading to improved performance in point cloud understanding. Additionally GPM demonstrates highly competitive results in unconditional point cloud generation tasks even exhibiting the potential for conditional generation tasks by modifying the input's conditional information. Compared to models like Point-BERT MaskPoint and PointMAE our GPM achieves superior performance in point cloud understanding tasks. Furthermore the integration of autoregressive and autoencoding within the same transformer underscores its versatility across different downstream tasks.

While recent advances in neural radiance field enable realistic digitization for large-scale scenes the image-capturing process is still time-consuming and labor-intensive. Previous works attempt to automate this process using the Next-Best-View (NBV) policy for active 3D reconstruction. However the existing NBV policies heavily rely on hand-crafted criteria limited action space or per-scene optimized representations. These constraints limit their cross-dataset generalizability. To overcome them we propose GenNBV an end-to-end generalizable NBV policy. Our policy adopts a reinforcement learning (RL)-based framework and extends typical limited action space to 5D free space. It empowers our agent drone to scan from any viewpoint and even interact with unseen geometries during training. To boost the cross-dataset generalizability we also propose a novel multi-source state embedding including geometric semantic and action representations. We establish a benchmark using the Isaac Gym simulator with the Houses3K and OmniObject3D datasets to evaluate this NBV policy. Experiments demonstrate that our policy achieves a 98.26% and 97.12% coverage ratio on unseen building-scale objects from these datasets respectively outperforming prior solutions.

We present GenN2N a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing colorization super-resolution inpainting etc. Unlike previous methods designed for individual translation tasks with task-specific schemes GenN2N achieves all these NeRF editing tasks by employing a plug-and-play image-to-image translator to perform editing in the 2D domain and lifting 2D edits into the 3D NeRF space. Since the 3D consistency of 2D edits may not be assured we propose to model the distribution of the underlying 3D edits through a generative model that can cover all possible edited NeRFs. To model the distribution of 3D edited NeRFs from 2D edited images we carefully design a VAE-GAN that encodes images while decoding NeRFs. The latent space is trained to align with a Gaussian distribution and the NeRFs are supervised through an adversarial loss on its renderings. To ensure the latent code does not depend on 2D viewpoints but truly reflects the 3D edits we also regularize the latent code through a contrastive learning scheme. Extensive experiments on various editing tasks show GenN2N as a universal framework performs as well or better than task-specific specialists while possessing flexible generative power. More results on our project page: https://xiangyueliu.github.io/GenN2N/.

Extensions of Neural Radiance Fields (NeRFs) to model dynamic scenes have enabled their near photo-realistic free-viewpoint rendering. Although these methods have shown some potential in creating immersive experiences two drawbacks limit their ubiquity: (i) a significant reduction in reconstruction quality when the computing budget is limited and (ii) a lack of semantic understanding of the underlying scenes. To address these issues we introduce Gear-NeRF which leverages semantic information from powerful image segmentation models. Our approach presents a principled way for learning a spatio-temporal (4D) semantic embedding based on which we introduce the concept of gears to allow for stratified modeling of dynamic regions of the scene based on the extent of their motion. Such differentiation allows us to adjust the spatio-temporal sampling resolution for each region in proportion to its motion scale achieving more photo-realistic dynamic novel view synthesis. At the same time almost for free our approach enables free-viewpoint tracking of objects of interest -- a functionality not yet achieved by existing NeRF-based methods. Empirical studies validate the effectiveness of our method where we achieve state-of-the-art rendering and tracking performance on multiple challenging datasets. The project page is available at: https://merl.com/research/highlights/gear-nerf.

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.

Recently impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However current diffusion models primarily generate images by predicting noise in the latent space and the editing is usually applied to the whole image which makes it challenging to perform delicate especially localized editing for 3D scenes. Inspired by recent 3D Gaussian splatting we propose a systematic framework named GaussianEditor to edit 3D scenes delicately via 3D Gaussians with text instructions. Benefiting from the explicit property of 3D Gaussians we design a series of techniques to achieve delicate editing. Specifically we first extract the region of interest (RoI) corresponding to the text instruction aligning it to 3D Gaussians. The Gaussian RoI is further used to control the editing process. Our framework can achieve more delicate and precise editing of 3D scenes than previous methods while enjoying much faster training speed i.e. within 20 minutes on a single V100 GPU more than twice as fast as Instruct-NeRF2NeRF (45 minutes -- 2 hours). The project page is at GaussianEditor.github.io.

In recent times the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency but their quality and generalization are limited as trainable 3D data is expensive and hard to obtain. 2D diffusion models enjoy strong abilities of generalization and fine generation but 3D consistency is hard to guarantee. This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation. A fast 3D object generation framework named as GaussianDreamer is proposed where the 3D diffusion model provides priors for initialization and the 2D diffusion model enriches the geometry and appearance. Operations of noisy point growing and color perturbation are introduced to enhance the initialized Gaussians. Our GaussianDreamer can generate a high-quality 3D instance or 3D avatar within 15 minutes on one GPU much faster than previous methods while the generated instances can be directly rendered in real time. Demos and code are available at https://taoranyi.com/gaussiandreamer/.

We introduce GaussianAvatars a new method to create photorealistic head avatars that are fully controllable in terms of expression pose and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model e.g. through expression transfer from a driving sequence or by manually changing the morphable model parameters. We parameterize each splat by a local coordinate frame of a triangle and optimize for explicit displacement offset to obtain a more accurate geometric representation. During avatar reconstruction we jointly optimize for the morphable model parameters and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios. For instance we show reenactments from a driving video where our method outperforms existing works by a significant margin.

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

We present GauHuman a 3D human model with Gaussian Splatting for both fast training (1 2 minutes) and real-time rendering (up to 189 FPS) compared with existing NeRF-based implicit representation modelling frameworks demanding hours of training and seconds of rendering per frame. Specifically GauHuman encodes Gaussian Splatting in the canonical space and transforms 3D Gaussians from canonical space to posed space with linear blend skinning (LBS) in which effective pose and LBS refinement modules are designed to learn fine details of 3D humans under negligible computational cost. Moreover to enable fast optimization of GauHuman we initialize and prune 3D Gaussians with 3D human prior while splitting/cloning via KL divergence guidance along with a novel merge operation for further speeding up. Extensive experiments on ZJU_Mocap and MonoCap datasets demonstrate that GauHuman achieves state-of-the-art performance quantitatively and qualitatively with fast training and real-time rendering speed. Notably without sacrificing rendering quality GauHuman can fast model the 3D human performer with 13k 3D Gaussians. Our code is available at https://github.com/skhu101/GauHuman.

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However existing methods that recover scene properties such as geometry appearance or radiance solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly recovering scenes with scanning lidar sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods

Utilizing multi-view inputs to synthesize novel-view images Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work we introduce a Generalizable Semantic Neural Radiance Field (GSNeRF) which uniquely takes image semantics into the synthesis process so that both novel view images and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic Geo-Reasoning and Depth-Guided Visual rendering. The former is able to observe multi-view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified.

In this paper we introduce GS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Specifically we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover in the pose tracking process an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica TUM-RGBD datasets. Project page: \href https://gs-slam.github.io/ https://gs-slam.github.io/ .