Human-centric volumetric videos offer immersive free-viewpoint experiences, yet existing methods focus either on replaying general dynamic scenes or animating human avatars, limiting their ability to re-perform general dynamic scenes. In this paper, we present RePerformer, a novel Gaussian-based representation that unifies playback and re-performance for high-fidelity human-centric volumetric videos. Specifically, we hierarchically disentangle the dynamic scenes into motion Gaussians and appearance Gaussians which are associated in the canonical space. We further employ a Morton-based parameterization to efficiently encode the appearance Gaussians into 2D position and attribute maps. For enhanced generalization, we adopt 2D CNNs to map position maps to attribute maps, which can be assembled into appearance Gaussians for high-fidelity rendering of the dynamic scenes. For re-performance, we develop a semantic-aware alignment module and apply deformation transfer on motion Gaussians, enabling photo-real rendering under novel motions. Extensive experiments validate the robustness and effectiveness of RePerformer, setting a new benchmark for playback-then-reperformance paradigm in human-centric volumetric videos. Project page: https://moqiyinlun.github.io/Reperformer/.

Volumetric video is rapidly emerging as a next-generation media format for immersive VR/AR applications, offering free-viewpoint rendering and unprecedented realism. While 3D Gaussian Splatting (3DGS) has recently demonstrated impressive rendering quality and real-time performance, existing dynamic extensions often struggle with long sequences due to the lack of efficient and codec-friendly compression schemes. In particular, current methods are not yet VR/AR-ready, as they fail to balance high-fidelity rendering, compact storage, and real-time decoding across heterogeneous platforms. To address these challenges, we propose Dynamic Gaussian Streams, a compact, video-compatible representation for real-time immersive playback. Given multi-view video inputs, we leverage the DualGS framework to reconstruct a temporally coherent 4D Gaussian sequence, introducing key modifications that directly optimize the 3D positions of dense skin Gaussians to improve compressibility and rendering quality. Each frame is converted into structured 2D maps, where key appearance attributes are compressed using per-channel codebooks with uint8 index maps. Hierarchical index reordering and Morton layout optimize spatial and temporal locality, ensuring compatibility with standard H.264 codecs. For spatial attributes like position, a lossless uint16 quantization preserves sub-pixel accuracy. Our system strikes a strong balance between compression and visual fidelity, enabling real-time decoding and immersive rendering on platforms including mobile devices, and XR headsets such as Apple Vision Pro.

Volumetric video is emerging as a key medium for digitizing the dynamic physical world, creating the virtual environments with six degrees of freedom to deliver immersive user experiences. However, robustly modeling general dynamic scenes, especially those involving topological changes while maintaining long-term tracking remains a fundamental challenge. In this paper, we present TaoGS, a novel topology-aware dynamic Gaussian representation that disentangles motion and appearance to support, both, long-range tracking and topological adaptation. We represent scene motion with a sparse set of motion Gaussians, which are continuously updated by a spatio-temporal tracker and photometric cues that detect structural variations across frames. To capture fine-grained texture, each motion Gaussian anchors and dynamically activates a set of local appearance Gaussians, which are non-rigidly warped to the current frame to provide strong initialization and significantly reduce training time. This activation mechanism enables efficient modeling of detailed textures and maintains temporal coherence, allowing high-fidelity rendering even under challenging scenarios such as changing clothes. To enable seamless integration into codec-based volumetric formats, we introduce a global Gaussian Lookup Table that records the lifespan of each Gaussian and organizes attributes into a lifespan-aware 2D layout. This structure aligns naturally with standard video codecs and supports up to 40× compression. TaoGS provides a unified, adaptive solution for scalable volumetric video under topological variation, capturing moments where “elegance in motion” and “Power in Stillness”— delivering immersive experiences that harmonize with the physical world. Project page: https://guochch.github.io/TaoGS/.

Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV-based inspection, and smart city digital twins. While aerial imagery offers broad coverage and complements limitations of ground-based data, reconstructing city-scale environments from such views remains challenging due to occlusions, incomplete geometry, and high memory demands. Recent advances like 3D Gaussian Splatting (3DGS) improve scalability and visual quality but remain limited by dense primitive usage, long training times, and poor suitability for edge devices. We propose CityGo, a hybrid framework that combines textured proxy geometry with residual and surrounding 3D Gaussians for lightweight, photorealistic rendering of urban scenes from aerial perspectives. Our approach first extracts compact building proxy meshes from MVS point clouds, then uses zero-order SH Gaussians to generate occlusion-free textures via image-based rendering and back-projection. To capture high-frequency details, we introduce residual Gaussians placed based on proxy-photo discrepancies and guided by depth priors. Broader urban context is represented by surrounding Gaussians, with importance-aware downsampling applied to non-critical regions to reduce redundancy. A tailored optimization strategy jointly refines proxy textures and Gaussian parameters, enabling real-time rendering of complex urban scenes on mobile GPUs with significantly reduced training and memory requirements. Extensive experiments on real-world aerial datasets demonstrate that our hybrid representation achieves fastest training speed, while delivering comparable visual fidelity to pure 3D Gaussian Splatting approaches. Furthermore, CityGo enables real-time rendering of large-scale urban scenes on mobile consumer GPUs, with substantially reduced memory usage and energy consumption.

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet, efficiently ren-dering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this pa-per, we present HiFi4G, an explicit and compact Gaussian-based approach for high-fidelity human performance ren-dering 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 en-force subsequent constraints. Then, we utilize a 4D Gaus-sian 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 demon-strate the effectiveness of our approach, which significantly outperforms existing approaches in terms of optimization speed, rendering quality, and storage overhead. Project page: https://nowheretrix.github.io/HiFi4G/.

Large garages are ubiquitous yet intricate scenes that present unique challenges due to their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle glass. Conventional Structure from Motion (SfM) methods for camera pose estimation and 3D reconstruction often fail in these environments due to poor correspondence construction. To address these challenges, we introduce LetsGo, a LiDAR-assisted Gaussian splatting framework for large-scale garage modeling and rendering. We develop a handheld scanner, Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate accurate data acquisition. Using this Polar device, we present the GarageWorld dataset, consisting of eight expansive garage scenes with diverse geometric structures, which will be made publicly available for further research. Our approach demonstrates that LiDAR point clouds collected by the Polar device significantly enhance a suite of 3D Gaussian splatting algorithms for garage scene modeling and rendering. We introduce a novel depth regularizer that effectively eliminates floating artifacts in rendered images. Additionally, we propose a multi-resolution 3D Gaussian representation designed for Level-of-Detail (LOD) rendering. This includes adapted scaling factors for individual levels and a random-resolution-level training scheme to optimize the Gaussians across different resolutions. This representation enables efficient rendering of large-scale garage scenes on lightweight devices via a web-based renderer. Experimental results on our GarageWorld dataset, as well as on ScanNet++ and KITTI-360, demonstrate the superiority of our method in terms of rendering quality and resource efficiency.

Volumetric video represents a transformative advancement in visual media, enabling users to freely navigate immersive virtual experiences and narrowing the gap between digital and real worlds. However, the need for extensive manual intervention to stabilize mesh sequences and the generation of excessively large assets in existing workflows impedes broader adoption. In this paper, we present a novel Gaussian-based approach, dubbed DualGS , for real-time and high-fidelity playback of complex human performance with excellent compression ratios. Our key idea in DualGS is to separately represent motion and appearance using the corresponding skin and joint Gaussians. Such an explicit disentanglement can significantly reduce motion redundancy and enhance temporal coherence. We begin by initializing the DualGS and anchoring skin Gaussians to joint Gaussians at the first frame. Subsequently, we employ a coarse-to-fine training strategy for frame-by-frame human performance modeling. It includes a coarse alignment phase for overall motion prediction as well as a fine-grained optimization for robust tracking and high-fidelity rendering. To integrate volumetric video seamlessly into VR environments, we efficiently compress motion using entropy encoding and appearance using codec compression coupled with a persistent codebook. Our approach achieves a compression ratio of up to 120 times, only requiring approximately 350KB of storage per frame. We demonstrate the efficacy of our representation through photo-realistic, free-view experiences on VR headsets, enabling users to immersively watch musicians in performance and feel the rhythm of the notes at the performers' fingertips. Project page: https://nowheretrix.github.io/DualGS/.

Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly on modeling static objects captured under multi-view settings where real-time rendering can be achieved with smart data structures, e.g., PlenOctree. In this paper, we present a novel Fourier PlenOctree (FPO) technique to tackle efficient neural mod-eling and real-time rendering of dynamic scenes captured under the free-view video (FVV) setting. The key idea in our FPO is a novel combination of generalized NeRF, PlenOctree representation, volumetric fusion and Fourier transform. To accelerate FPO construction, we present a novel coarse-to-fine fusion scheme that leverages the gen-eralizable NeRF technique to generate the tree via spatial blending. To tackle dynamic scenes, we tailor the implicit network to model the Fourier coefficients of time-varying density and color attributes. Finally, we construct the FPO and train the Fourier coefficients directly on the leaves of a union PlenOctree structure of the dynamic sequence. We show that the resulting FPO enables compact memory overload to handle dynamic objects and supports efficient fine-tuning. Extensive experiments show that the proposed method is 3000 times faster than the original NeRF and achieves over an order of magnitude acceleration over SOTA while preserving high visual quality for the free-viewpoint rendering of unseen dynamic scenes.

Recent neural human representations can produce high-quality multi-view rendering but require using dense multi-view inputs and costly training. They are hence largely limited to static models as training each frame is infeasible. We present HumanNeRF - a neural representation with efficient generalization ability - for high-fidelity free-view synthesis of dynamic humans. Analogous to how IBRNet assists NeRF by avoiding perscene training, HumanNeRF employs an aggregated pixel-alignment feature across multi-view inputs along with a pose embedded non-rigid deformation field for tackling dynamic motions. The raw Human-NeRF can already produce reasonable rendering on sparse video inputs of unseen subjects and camera settings. To further improve the rendering quality, we augment our solution with in-hour scene-specific fine-tuning, and an appearance blending module for combining the benefits of both neural volumetric rendering and neural texture blending. Extensive experiments on various multi-view dynamic hu-man datasets demonstrate effectiveness of our approach in synthesizing photo-realistic free-view humans under challenging motions and with very sparse camera view inputs.

We, humans, are entering into a virtual era and indeed want to bring animals to the virtual world as well for companion. Yet, computer-generated (CGI) furry animals are limited by tedious off-line rendering, let alone interactive motion control. In this paper, we present ARTEMIS, a novel neural modeling and rendering pipeline for generating ARTiculated neural pets with appEarance and Motion synthesIS. Our ARTEMIS enables interactive motion control, real-time animation, and photo-realistic rendering of furry animals. The core of our ARTEMIS is a neural-generated (NGI) animal engine, which adopts an efficient octree-based representation for animal animation and fur rendering. The animation then becomes equivalent to voxel-level deformation based on explicit skeletal warping. We further use a fast octree indexing and efficient volumetric rendering scheme to generate appearance and density features maps. Finally, we propose a novel shading network to generate high-fidelity details of appearance and opacity under novel poses from appearance and density feature maps. For the motion control module in ARTEMIS, we combine state-of-the-art animal motion capture approach with recent neural character control scheme. We introduce an effective optimization scheme to reconstruct the skeletal motion of real animals captured by a multi-view RGB and Vicon camera array. We feed all the captured motion into a neural character control scheme to generate abstract control signals with motion styles. We further integrate ARTEMIS into existing engines that support VR headsets, providing an unprecedented immersive experience where a user can intimately interact with a variety of virtual animals with vivid movements and photo-realistic appearance. Extensive experiments and showcases demonstrate the effectiveness of our ARTEMIS system in achieving highly realistic rendering of NGI animals in real-time, providing daily immersive and interactive experiences with digital animals unseen before. We make available our ARTEMIS model and dynamic furry animal dataset at https://haiminluo.github.io/publication/artemis/.

We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach for high-quality reconstruction, compression, and rendering of human performances from dense multi-view videos. Our core intuition is to bridge the traditional animated mesh workflow with a new class of highly efficient neural techniques. We first introduce a neural surface reconstructor for high-quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolution hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides the coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in our reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering under various bandwidth settings. To strike an intricate balance between quality and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experiences on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows with VR headsets.

4D reconstruction and rendering of human activities is critical for immersive VR/AR experience. Recent advances still fail to recover fine geometry and texture results with the level of detail present in the input images from sparse multi-view RGB cameras. In this paper, we propose Neural-HumanFVV, a real-time neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of human activities in arbitrary novel views. We propose a neural geometry generation scheme with a hierarchical sampling strategy for real-time implicit geometry inference, as well as a novel neural blending scheme to generate high resolution (e.g., 1k) and photo-realistic texture results in the novel views. Furthermore, we adopt neural normal blending to enhance geometry details and formulate our neural geometry and texture rendering into a multi-task learning framework. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality geometry and photo-realistic free view-point reconstruction for challenging human performances.

We present a structure-from-motion (SfM) framework based on a special type of multi-perspective camera called the cross-slit or XSlit camera. Traditional perspective camera based SfM suffers from the scale ambiguity which is inherent to the pinhole camera geometry. In contrast, an XSlit camera captures rays passing through two oblique lines in 3D space and we show such ray geometry directly resolves the scale ambiguity when employed for SfM. To accommodate the XSlit cameras, we develop tailored feature matching, camera pose estimation, triangulation, and bundle adjustment techniques. Specifically, we devise a SIFT feature variant using non-uniform Gaussian kernels to handle the distortions in XSlit images for reliable feature matching. Moreover, we demonstrate that the XSlit camera exhibits ambiguities in pose estimation process which can not be handled by existing work. Consequently, we propose a 14 point algorithm to properly handle the XSlit degeneracy and estimate the relative pose between XSlit cameras from feature correspondences. We further exploit the unique depth-dependent aspect ratio (DDAR) property to improve the bundle adjustment for the XSlit camera. Synthetic and real experiments demonstrate that the proposed XSlit SfM can conduct reliable and high fidelity 3D reconstruction at an absolute scale.

Generating free-viewpoint videos is critical for immersive VR/AR experience, but recent neural advances still lack the editing ability to manipulate the visual perception for large dynamic scenes. To fill this gap, in this paper, we propose the first approach for editable free-viewpoint video generation for large-scale view-dependent dynamic scenes using only 16 cameras. The core of our approach is a new layered neural representation, where each dynamic entity, including the environment itself, is formulated into a spatio-temporal coherent neural layered radiance representation called ST-NeRF. Such a layered representation supports manipulations of the dynamic scene while still supporting a wide free viewing experience. In our ST-NeRF, we represent the dynamic entity/layer as a continuous function, which achieves the disentanglement of location, deformation as well as the appearance of the dynamic entity in a continuous and self-supervised manner. We propose a scene parsing 4D label map tracking to disentangle the spatial information explicitly and a continuous deform module to disentangle the temporal motion implicitly. An object-aware volume rendering scheme is further introduced for the re-assembling of all the neural layers. We adopt a novel layered loss and motion-aware ray sampling strategy to enable efficient training for a large dynamic scene with multiple performers, Our framework further enables a variety of editing functions, i.e., manipulating the scale and location, duplicating or retiming individual neural layers to create numerous visual effects while preserving high realism. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality, photo-realistic, and editable free-viewpoint video generation for dynamic scenes.

There have been significant advances in capturing gigapixel panoramas (GPP). However, solutions for viewing GPPs on head-mounted displays (HMDs) are lagging: an immersive experience requires ultra-fast rendering while directly loading a GPP onto the GPU is infeasible due to limited texture memory capacity. In this paper, we present a novel out-of-core rendering technique that supports not only classic panning, tilting, and zooming but also dynamic refocusing for viewing a GPP on HMD. Inspired by the network package transmission mechanisms in distributed visualization, our approach employs hierarchical image tiling and on-demand data updates across the main and the GPU memory. We further present a multi-resolution rendering scheme and a refocused light field rendering technique based on RGBD GPPs with minimal memory overhead. Comprehensive experiments demonstrate that our technique is highly efficient and reliable, able to achieve ultra-high frame rates ( fps) even on low-end GPUs. With an embedded gaze tracker, our technique enables immersive panorama viewing experiences with unprecedented resolutions, field-of-view, and focus variations while maintaining smooth spatial, angular, and focal transitions.

Reconstructing a human portrait in a realistic and convenient manner is critical for human modeling and understanding. Aiming at light-weight and realistic human portrait reconstruction, in this paper we propose Neural3D: a novel neural human portrait scanning system using only a single RGB camera. In our system, to enable accurate pose estimation,we propose a context-aware correspondence learning approach which jointly models the appearance, spatial and motion information between feature pairs. To enable realistic reconstruction and suppress the geometry error, we further adopt a point-based neural rendering scheme to generate realistic and immersive portrait visualization in arbitrary virtual view-points. By introducing these learning-based technical components into the pure RGB-based human modeling framework, we can achieve both accurate camera pose estimation and realistic free-viewpoint rendering of the reconstructed human portrait. Extensive experiments on a variety of challenging capture scenarios demonstrate the robustness and effectiveness of our approach.

Reliable human parts segmentation on 2D images plays an important role in many human-centric computer vision tasks. While significant achievements have been made on human pose estimation, the performance on human parts segmentation remains low. In this paper, we present a novel technique that we call Pose2Body that robustly conducts human parts segmentation based on the pose estimation results. We partition an image into superpixels and set out to assign a segment label to each superpixel most consistent with the pose. We design special feature vectors for every superpixel-label assignment as well as superpixel-superpixel pairs and model optimal labeling as to solve for a conditional random field (CRF). Comprehensive experiments show that our technique achieves substantial improvements over the state-of-the-art solutions.

We present a novel light field structure-from-motion (SfM) framework for reliable 3D object reconstruction. Specifically, we use the light field (LF) camera such as Lytro and Raytrix as a virtual 3D scanner. We move an LF camera around the object and register between multiple LF shots. We show that applying conventional SfM on sub-aperture images is not only expensive but also unreliable due to ultra-small baseline and low image resolution. Instead, our LF-SfM scheme maps ray manifolds across LFs. Specifically, we show how rays passing through a common 3D point transform between two LFs and we develop reliable technique for extracting extrinsic parameters from this ray transform. Next, we apply a new edge-preserving stereo matching technique on individual LFs and conduct LF bundle adjustment to jointly optimize pose and geometry. Comprehensive experiments show our solution outperforms many state-of-the-art passive and even active techniques especially on topologically complex objects.

Traditional Structure-from-Motion (SfM) uses images captured by cameras as inputs. In this paper, we explore using light fields captured by plenoptic cameras or camera arrays as inputs. We call this solution plenoptic SfM or P-SfM solution. We first present a comprehensive theory on ray geometry transforms under light field pose variations. We derive the transforms of three typical ray manifolds: rays passing through a point or point-ray manifold, rays passing through a 3D line or ray-line manifold, and rays lying on a common 3D plane or ray-plane manifold. We show that by matching these manifolds across LFs, we can recover light field poses and conduct bundle adjustment in ray space. We validate our theory and framework on synthetic and real data on light fields of different scales: small scale LFs acquired using a LF camera and large scale LFs by a camera array. We show that our P-SfM technique can significantly improve the accuracy and reliability over regular SfM and PnP especially on traditionally challenging scenes where reliable feature point correspondences are difficult to obtain but line or plane correspondences are readily accessible.