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Imaging objects hidden outside the direct line of sight expands the effective field of view and is critical for applications such as autonomous driving and robotic perception. Despite impressive progress in time-of-flight (ToF)-based non-line-of-sight (NLOS) imaging, real-world deployment remains challenging because practical measurements are often collected over spatially limited, arbitrarily shaped relay regions—conditions that violate the planar-wall and dense-sampling assumptions made by most existing methods. To address these limitations, we propose a LOS-guided NLOS imaging pipeline that imposes no geometric assumptions on the relay surface and naturally supports both confocal and non-confocal configurations. Our method represents the hidden scene using 3D Gaussian primitives and couples them with an efficient, differentiable transient rendering model, enabling end-to-end optimization directly from measured transients. We validate our approach on real-world measurements from both a public dataset and a custom-built capture system. Across settings, our method achieves state-of-the-art reconstruction fidelity under spatially limited, sparsely sampled conditions, and significantly outperforms existing methods on complex, arbitrary relay surface geometries. We release our code and datasets at nlos-3d-gtr.
Simulating free-surface flows requires capturing the effects of incompressibility, viscosity, and surface tension. Existing particle-based methods often rely on operator splitting, which introduces coupling artifacts and limits stability. We propose a unified nonlinear optimization framework that achieves a strong coupling of these three effects within a single solver. By leveraging peridynamics, we formulate the discretization of distinct fluid mechanisms under a consistent variational principle. Specifically, we recast fluid motion as a nonlinear variational optimization problem over particle positions, which is solved via the semi-implicit successive substitution method. Moreover, the framework incorporates separate treatments for bulk and shear viscosity, allowing for more refined control of different viscous fluid behaviors. To the best of our knowledge, this is the first particle-based unified solver capable of fully resolving the interdependence of incompressibility, viscosity, and surface tension, thereby significantly enhancing stability in complex simulations of free-surface flows. The source code for the paper is publicly available at https://github.com/peridyno/peridyno.
The Material Point Method (MPM) provides a unified framework for simulating multi-material systems but struggles to allow for the separation of objects. Traditionally, multi-material simulations use a single background grid, which allows for automatic interaction between materials. Since particles of different types receive their velocities by interpolating from a common background grid, they are unable to efficiently separate. This is especially problematic when simulating immiscible fluids such as oil and water; once oil particles and water particles become mixed, they are unable to separate. A common solution is to solve different objects or materials on separate background grids, which allows particles of different materials to move with different velocities and separate. Using separate background grids loses the natural interaction between materials, since materials no longer interact through the background grid. In the case of immiscible fluids, phase separation is driven by thermodynamics and buoyancy. We present a novel method that enables natural buoyancy-driven phase separation in weakly compressible MPM. Our key observation is that compression and pressure do not depend on the type of particle. A local neighborhood of particles is compressed because particles are close together, and they exert pressure because they bump into the particles that are nearby. Correspondingly, we update the deformation gradient from a unified velocity field and use it to compute a unified pressure force. Combined with the use of separate background grids for velocity, this leads to a treatment of immiscible multiphase fluids that naturally separates due to buoyancy forces, even from a fully mixed configuration. We also propose a mixing potential that is capable of driving phase separation caused by thermodynamics even in the absence of gravity. Finally, we propose a novel algorithm for obtaining consistent per-phase level sets for rendering multiphase particle-based fluids.
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We propose a robust texture optimization framework that handles inconsistent AI-generated targets by operating directly within non-differentiable production pipelines. Standard optimization approaches struggle with inconsistent targets, often producing blurry textures and ghosting artifacts. Moreover, existing inverse rendering methods rely on differentiable renderers, causing a rendering gap when assets are deployed in production engines. To address the inconsistencies, we introduce a robust formulation that jointly optimizes a deformation grid and an uncertainty map. This formulation effectively decouples geometric misalignment from semantic hallucinations. To avoid the rendering gap, we leverage finite-difference gradient estimation to operate entirely within standard rasterizers. Extensive experiments demonstrate that our approach recovers sharp, high-fidelity textures from inconsistent AI-generated targets, achieving higher visual quality than existing methods. This makes our technique a practical tool for film production, gaming, and virtual reality, where flexibility and visual quality are paramount.
Smith microfacet models are widely used to describe the interaction of light with surfaces. However, conventional microfacet models account for only a single bounce on microsurfaces, which leads to energy loss. To address the problem of missing energy, several unbiased methods have been proposed to compute multiple bounces on Smith microsurfaces. In addition, a position-free multiple-bounce microfacet model has been introduced by assuming that each bounce of a light path is height-independent, leading to lower variance but biased results. In this paper, we propose a novel derivation of multiple-bounce Smith microfacet bidirectional reflectance distribution functions (BRDFs) based on a partial adoption of the independent-bounce assumption. Our model treats most bounces as height-interdependent, leading to a height-interdependent path formulation. We further propose a successive height preintegration to make this formulation position-free. Although our model still exhibits an extremely small bias, it achieves lower variance than prior unbiased methods. Furthermore, we introduce a path type-aware BRDF evaluation that further reduces variance.
Traditional physically-based material models rely on analytically derived bidirectional reflectance distribution functions (BRDFs), typically by considering statistics of micro-primitives such as facets, flakes, or spheres, sometimes combined with multi-bounce interactions such as layering and multiple scattering. These derivations are often complex and model-specific. Once an analytic BRDF evaluation is defined, one still needs to design an importance sampling method for it and evaluate the probability density function (pdf) of that sampling distribution, requiring further model-specific derivations. We present PureSample: a novel neural BRDF representation that allows learning a material’s appearance purely by sampling forward random walks on the microgeometry, which is usually straightforward to implement. Our representation allows for efficient BRDF evaluation, importance sampling, and pdf evaluation, for homogeneous as well as spatially varying materials. We achieve this by two learnable components: first, the sampling distribution is modeled using a flow matching neural network, which allows both importance sampling and pdf evaluation; second, we introduce a view-dependent albedo term, captured by a lightweight neural network, which allows for converting a pdf value to a BRDF value for any pair of view and light directions. We demonstrate PureSample on challenging materials, including various microgeometries, multi-layered materials, and multiple-scattering microfacet materials.
Realistic integration of user-specified textures into scene images is a fundamental task in computer graphics and image editing. While existing material transfer and reference-guided inpainting methods can edit surface appearances, they often fail to address the specific requirements of texture tiling. This task necessitates precisely repeating a reference pattern according to user-defined parameters such as frequency, orientation, and scale. Furthermore, current generative approaches often struggle to maintain the structural fidelity of the reference texture, limited by either destructive pixel-level resampling or the lack of fine-grained spatial information in semantic image encoders, and they frequently fail to preserve the coherent lighting and geometry of the original scene. In this paper, we propose a novel framework for controllable and high-fidelity texture tiling based on Diffusion Transformers. Our approach introduces two key technical innovations to decouple spatial manipulation from content generation. First, we propose a Coordinate-Transformed Rotary Embedding mechanism. By applying 2D affine transformations directly to the relative positional embeddings between the target latent and the image condition, we achieve precise control over tiling patterns without explicit pixel warping, thereby utilizing the full information of the reference condition without degradation. Second, a Disjoint Attention Mask is employed to shield reference features from semantic leakage. This preserves structural integrity while seamlessly blending the synthesized texture with the scene’s original lighting and geometry. Extensive experiments demonstrate that our method outperforms state-of-the-art baselines in both control accuracy and texture fidelity. The code and dataset are publicly accessible at https://github.com/junrongh/ControlTile.
Time-varying scene meshes are a widely used representation for volumetric video, offering immersive six degrees of freedom (6DoF) interaction in virtual environments. However, their significantly larger size presents major challenges for efficient storage, Internet transmission, and real-time streaming. Existing mesh compression standards are not well-suited for complex scene meshes with dynamic content, and no standardized encoding techniques have been widely adopted for this class of data. To address this gap, we propose TSMC, a novel compression framework for 3D time-varying scene mesh sequences. TSMC uses voxel-based analysis to establish temporally stable correspondences, segment static and dynamic regions, and extract volume-tracked reference meshes for dynamic content to support inter-frame prediction. Static backgrounds and reference meshes are compressed using Google Draco, while dynamic displacements are encoded using a combination of the Karhunen-Loève Transform (KLT) and Laplacian coordinates. Extensive experiments demonstrate that at 30 fps and an SSIM of 0.9, TSMC reduces bandwidth usage and decoding time by up to 56.54% and 85.42%, respectively, compared to state-of-the-art mesh compression methods. Our code and dataset are available at https://github.com/SINRG-Lab/TSMC.
Volumetric video (VV) streaming delivers truly immersive viewing experiences over the Internet, serving as a critical foundation for next-generation applications, including immersive telepresence in the metaverse, the surveillance of remote ecological systems, and robotic teleoperation for embodied AI, and beyond. Beyond immersive viewing, these applications turn VV streaming into a real-time interface to remote physical environments, imposing new system-level demands for photorealistic scene representation, low-latency interaction, and robust performance under heterogeneous network conditions. 3D Gaussian Splatting (3DGS) has been widely used for real-time photorealistic rendering, offering superior visual quality and rendering performance, but it faces challenges due to bandwidth consumption. Furthermore, as the foundation of adaptive VV streaming, existing Levels of Detail (LoD) methods based on density are not well-suited to Gaussian representations, leading to visible gaps and severe quality degradation. Recent studies have also explored attribute compression techniques to reduce bandwidth consumption. Our preliminary studies reveal that aggressive attribute compression primarily causes color distortion, which can be effectively corrected in the rendered image using a reference image. Motivated by these findings, we propose a novel Color-Adaptive scheme for adaptive VV streaming that uses vector quantization (VQ) to establish LoDs and correct color distortions with low-resolution reference images. We further present CAGS, an adaptive VV streaming system compatible with diverse Gaussian representations, which integrates the Color-Adaptive scheme by rendering reference images on the streaming server and performing color restoration on the client. Extensive experiments on our prototype system demonstrate that CAGS outperforms the existing adaptive streaming systems in PSNR by 5 ∼ 20 dB under fluctuating bandwidth, operates significantly faster than existing scalable Gaussian compression methods, and generalizes across different Gaussian representations. The code is available at https://github.com/yindaheng98/ColorAdaptiveGaussianSplatting.
We present an empirical study of free-view video compression for dynamic scenes reconstructed with 3D Gaussian Splatting, examining how practical pipeline design choices affect reconstruction fidelity and storage efficiency. Rather than introducing new representations, we analyze how commonly used components, including temporal chunking, deformation-based reconstruction, and quantization-aware training, interact in practice. We observe that partitioning long sequences into shorter temporal segments, such as GOPs, simplifies optimization and improves reconstruction fidelity, but can introduce additional storage overhead. We further show that encouraging smooth motion vectors across both space and time produces deformation signals that are easier for standard video codecs to compress, leading to improved rate–distortion performance. When integrated into a unified pipeline, these design choices consistently benefit different deformation-based reconstruction methods. Across multiple datasets, our approach achieves 20% storage reduction compared with state-of-the-art methods while preserving or improving visual quality, and we discuss sources of variability and ambiguity in current training and evaluation protocols.
Meshes are among the most common 3D scene representations, but directly generating meshes is challenging largely because the mesh representation contains many structures, such as permutation invariance of vertices or faces. To address this challenge, we present a novel approach that learns to generate triangle meshes represented as triangle soups. We adopt equivariant optimal-transport flow matching models that respect key symmetries within the triangle soup representation, including permutation invariance among faces and among vertices within each of the faces. Toward this goal, we propose a simple yet effective modification to the state-of-the-art Diffusion Transformer architecture, resulting in a scalable network capable of modeling a flow field while maintaining the desirable symmetries (equivariance). Moreover, we introduce a loss function grounded in optimal transport principles that improves model convergence by eliminating training signals that violate these symmetries. Our model can achieve performance comparable to state-of-the-art auto-regressive mesh generators while providing about an 18× speedup during inference.
Camera-controlled video generation is valuable for applications ranging from visual design to providing 2D supervision for 4D generation tasks. However, existing approaches are limited to single-trajectory generation, forcing users to process multiple trajectories in separate batches. This serial inference introduces content inconsistencies across viewpoints due to the inherent randomness of diffusion models. Explicit point cloud methods can only partially address this problem, as single-viewpoint back-projection suffers from sparsity and depth estimation errors. We propose CameraSquad, a multi-trajectory camera control framework that supports both single-trajectory and parallel multi-trajectory generation. Our method achieves precise camera control while preserving input video content through decoupled content and camera control mechanisms. To ensure viewpoint consistency in multi-trajectory mode, we design a dual-mode cross-view attention mechanism that maintains consistency across parallel trajectories while guaranteeing camera control precision. Extensive experiments demonstrate that CameraSquad achieves competitive performance in camera control accuracy, consistency maintenance, and generation quality compared to existing approaches. Our project page is available at https://rabberk.github.io/CameraSquad/.