Despite transformative advances in generative motion synthesis, real-time interactive motion control remains dominated by traditional techniques. In this work, we identify two key challenges in bridging research and production: 1) Real-time scalability: Industry applications demand real-time generation of a vast repertoire of motion skills, while generative methods exhibit significant degradation in quality and scalability under real-time computation constraints, and 2) Integration: Industry applications demand fine-grained multi-modal control involving velocity commands, style selection, and precise keyframes, a need largely unmet by existing text- or tag-driven models. Moreover, a systematic motion design interface for generative models remains absent. To overcome these limitations, we introduce MotionBricks: a large-scale, real-time generative framework with a two-fold solution. First, we propose a large-scale modular latent generative backbone tailored for robust real-time motion generation, effectively modeling a dataset of over 350,000 motion clips with a single model. Second, we introduce smart primitives that provide a unified, robust, and intuitive interface for authoring both navigation and object interaction. Notably, MotionBricks applies to new downstream tasks in a zero-shot manner, where no fine-tuning or task-specific tagging is required. Applications can be designed in a plug-and-play manner like assembling bricks without expert animation knowledge, enabling an accessible interface for applications in animation and robotics. Quantitatively, we show that MotionBricks produces state-of-the-art motion quality on open-source and proprietary datasets of various scales, while also achieving a real-time throughput of 15,000 FPS with 2ms latency. We demonstrate the flexibility and robustness of MotionBricks in a complete production-level animation demo, covering navigation and object-scene interaction across various styles with a unified model. To showcase our framework's application beyond animation, we deploy MotionBricks on the Unitree G1 humanoid robot to demonstrate its flexibility and generalization for real-time robotic control.
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Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual characters. Adversarial imitation learning has been a highly effective method for learning motion priors from reference motion data. However, adversarial priors, with few exceptions, need to be retrained for each new controller, thereby limiting their reusability and necessitating the retention of the reference motion data when applied to downstream tasks. In this work, we present Score-Matching Motion Priors (SMP), which leverages pre-trained motion diffusion models and score distillation sampling (SDS) to create reusable task-agnostic motion priors. SMPs can be pre-trained on a motion dataset, independent of any control policy or task. Once trained, SMPs can be kept frozen and reused as general-purpose reward functions to train new policies to produce naturalistic behaviors for downstream tasks. We show that a general motion prior trained on large-scale datasets can be repurposed into a variety of style-specific priors. Furthermore, SMP can compose different styles to synthesize new styles not present in the original dataset. Our method can create reusable and modular motion priors that produce high-quality motions comparable to state-of-the-art adversarial imitation learning methods. In our experiments, we demonstrate the effectiveness of SMP across a diverse suite of control tasks with physically simulated humanoid characters. Video available at youtu.be/jBA2tWk6vzU.
Neutron transport simulation plays a central role in nuclear engineering, yet its inverse problems—such as shielding design and sensitivity analysis—remain extremely challenging. The governing transport equation shares a common mathematical foundation with the volume rendering equation used for participating media in computer graphics. Recent advances in differentiable rendering have demonstrated that inverse problems—such as recovering scene properties from observations—can be efficiently solved through gradient-based optimization, where the gradients are estimated through Monte Carlo methods. Inspired by these developments, we extend differentiable rendering techniques to neutron transport problems, enabling inverse design and sensitivity analysis in nuclear systems. To adapt these methods to the neutron transport problem, we generalize the automatic differentiation of Monte Carlo light transport simulation to multi-energy neutron transport; propose a new simplified way to handle moving geometric discontinuities; and propose new estimators for derivatives of volume integrals. We demonstrate the effectiveness of the proposed framework on several model inverse problems, including gradient-based optimization of multi-group neutron moderation, sensitivity analysis of material properties, and diagnostic sensor shape optimization. These results show that differentiable Monte Carlo neutron transport can support optimization and analysis tasks, opening new opportunities for inverse design in nuclear shielding and related applications.
We propose a system to optimize parametric designs subject to radiation pressure, i.e., the effect of light on the motion of objects. This is most relevant in the design of spacecraft, where radiation pressure presents the dominant non-conservative forcing mechanism, which is the case beyond approximately 800 km altitude. Despite its importance, the high computational cost of high-fidelity radiation pressure modeling has limited its use in large-scale spacecraft design, optimization, and space situational awareness applications. We enable this by offering three innovations in the simulation, in representation and in optimization: First, a practical computer graphics-inspired Monte-Carlo (MC) simulation of radiation pressure. The simulation is highly parallel, uses importance sampling and next-event estimation to reduce variance and allows simulating an entire family of designs instead of a single spacecraft as in previous work. Second, we introduce neural networks as a representation of forces from design parameters. This neural proxy model, learned from simulations, is inherently differentiable and can query forces orders of magnitude faster than a full MC simulation. Third, and finally, we demonstrate optimizing inverse radiation pressure designs, such as finding geometry, material or operation parameters that minimizes travel time, maximizes proximity given a desired end-point, minimize thruster fuel, trains mission control policies or allocated compute budget in extraterrestrial compute.
Capturing the full plenoptic light transport across spatial, angular, and temporal dimensions has long been a pursuit in computational imaging, yet it remains fundamentally constrained by the high dimensionality of the sampling space and the physical inaccessibility of scene regions due to self-occlusions. While time-resolved imaging records the temporal axis, existing methods are bottlenecked by the combinatorial complexity of the plenoptic function. This high dimensionality makes dense omni-dimensional sampling physically prohibitive. Simultaneously, tight coupling between illumination and viewpoint in current systems also precludes the full acquisition of plenoptic light transport. In this work, we present GenPIE, a Generative Plenoptic Imager designed to bridge the gap between sparse physical observations and high-dimensional light transport. We introduce a decoupled laser-detector hardware setup that enables independent control over illumination and detection, allowing for active probing of indirect light paths. To overcome the ill-posedness of sparse sampling and physical blind spots, we propose a generative inverse transient rendering framework. Our approach leverages 3D foundation models to provide strong semantic and 3D geometric priors for initialization, which are subsequently refined through a differentiable transient path tracer to ensure physically grounded adherence to the Transient Rendering Equation. We demonstrate that GenPIE supports a range of applications that are challenging for steady-state or purely neural methods, including disentangling multi-bounce light transport directly from captured transient videos, time unwarping, and time-resolved relighting. The project page is at https://wangzh1.github.io/GenPIE.
Two-phase flows are ubiquitous in nature, exhibiting complex fluid-fluid interactions that challenges numerical simulators. To accurately and efficiently solve two-phase flows, grid-based methods have been widely adopted. Navier-Stokes (NS) solvers consume a small memory footprint, but simultaneously achieving both high performance and low numerical dissipation remains a significant challenge. In contrast, lattice Boltzmann solvers are efficient and have low numerical dissipation, yet they remain memoryintensive, even with state-of-the-art moment-encoding schemes. To date, the simultaneous attainment of high accuracy, exceptional efficiency, and a low memory footprint remains a major challenge in the field. In this paper, we propose a novel two-phase flow solver that achieves this objective. Our work is motivated by extending gas-kinetic scheme (GKS), which is adapted to handle nearly incompressible flows. To allow stable and accurate two-phase flow simulations, we systematically derive a coupled formulation of the GKS method and the phase-field model, incorporating novel mathematical constructs. Combined with robust boundary treatments and specialized techniques for handling turbulent flows, this results a unified framework capable of efficiently simulating two-phase flows, even those with large density contrasts and high Reynolds numbers. Since our formulation is explicit, it achieves exceptional performance when optimized on GPU, making it the fastest kinetic two-phase flow solver to date. Additionally, as it is derived from GKS, it obviates the need to store distribution functions. Thus, it has a small memory footprint, competitive with, or even lower than, that of many NS solvers. As a result, our solver can efficiently simulate complex two-phase flow dynamics at high resolutions using a single commodity GPU. We validate the accuracy of our solver via several benchmark tests, compare its performance with recent methods in various aspects, and demonstrate its capability to replicate a broad range of two-phase flow phenomena, encompassing both typical and large-scale scenarios.
We present Mixwell, a family of sharp 2D fluid brushes and GPU-accelerated analytical methods for progressive, resolution-independent physics-based mixing. Derived from idealized potential flow around a cylindrical tine, Mixwell includes a cusped, Kelvinlet-style regularized velocity brush that parsimoniously captures cylinder-fluid interactions. Building on Maxwell's 1869 drift formulation, we develop GPU-friendly evaluation strategies for particle drift in both infinite and finite strokes. For image-based workflows, we introduce cylindrical Reverse-Drift Functions (RDFs), displacement fields that encode tine insertion, motion, and removal. Like signed distance fields in geometric modeling, RDFs compose naturally and can be chained in shaders to model complex operations while avoiding intermediate texture blur. We also propose a periodicity-exploiting composition scheme for complex RDF patterns. All Mixwell operations are evaluated independently per sample, enabling truly progressive mixing and rendering without global solves, grids, or intermediate texture resampling. We demonstrate real-time GLSL and HLSL implementations and production integrations in Houdini (OpenCL, OSL), enabling progressive, arbitrary-resolution mixing and rendering with negligible numerical dissipation.
Mountainous terrains evolve over geological timescales through erosion processes driven by the complex interplay of transported quantities such as water, sediment, and rockfall. A key challenge in erosion modeling is the simultaneous simulation of transport and erosive processes, which differ in temporal scales by several orders of magnitude. We address this challenge with a novel, parallel, stochastic particle-based method capable of simulating transport over geological timescales. Our approach relaxes the strong assumptions on velocity required by prior works (e.g., based on the Stream Power Law), enabling a new erosion model grounded in a more general form of momentum conservation. We demonstrate that our scheme accurately solves the underlying conservation laws and avoids artifacts common in previous works. Furthermore, we show that our new erosion model captures multiscale geomorphological features, producing coherent basin structures and dynamic phenomena such as braided rivers, meanders, and deltas.
Simulating the dynamic, multiscale interactions between granular materials and multiphase fluids remains a significant computational challenge in computer graphics, as the visual complexity of such mixtures arises from strongly coupled small-scale structures. We present a novel, physically-based simulation framework for sand-water-air mixtures that couples a Lattice Boltzmann Method (LBM) for weakly-compressible two-phase fluids with a Material Point Method (MPM) for granular sand. Our approach is built upon a unified continuum formulation that expresses the governing equations for both fluid phases (air and water) and the granular medium within a consistent framework. To accurately capture the transition of sand from a dry, friction-dominated state to a soaked, sticky medium, we introduce a water retention model that describes how liquid infiltrates and is retained within the granular structure. Furthermore, we enforce volume conservation of the fluids within the mixture, ensuring numerical stability and physical realism. Our robust coupling mechanism enables the simulation of complex phenomena such as sand mobilization, transport, settling, and erosion across a wide range of density ratios. We demonstrate the efficiency of our method through several challenging scenarios, including the breaching of sand-walled basins, sediment-laden flows, and the erosive collapse of sand structures.
We present ST-FLIP, a spatiotemporal extension of the Fluid-Implicit Particle (FLIP) method for incompressible free-surface and two-phase liquid simulation. ST-FLIP enables time steps up to an order of magnitude larger than those typically used in CFL-constrained solvers, while preserving detailed flow structures and visual fidelity. It addresses a common failure mode of large time steps in hybrid particle-grid liquid solvers: temporal under-sampling of particle motion produces aliasing-driven free-surface artifacts after projection. Our key idea is to interpret particles as samples in four-dimensional space-time: in addition to standard spatial jittering, we randomize particle positions along the time axis as well and perform particle-to-grid deposition using a separable 4D kernel. This yields a Monte Carlo estimator of perstep time-slab-integrated particle quantities. Although particles are treated as samples in 4D space-time, our approach works as a lightweight plugin by collapsing to slab-integrated 3D grid fields for projection. Building on recent particle-based phase-field work, we reuse the particle-to-grid weight accumulators as a conceptual space-time phase field, providing variable-coefficient projection weights and eliminating the need for per-step surface reconstruction. The method can be easily integrated into existing FLIP/PIC or APIC solvers with negligible additional computational cost per time step. The effectiveness of our approach is demonstrated through a series of comparisons with state-of-the-art solvers, yielding several-fold speedups for multi-billion-particle simulations at high effective 3D resolutions on a single workstation.
Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token ordering strategies employed by existing methods typically fail to meet professional artist standards, where coordinate-based sorting yields inefficiently long sequences, and patch-based heuristics disrupt the continuous edge flow and structural regularity essential for high-quality modeling. To address these limitations, we propose Strips as Tokens (SATO), a novel framework with a token ordering strategy inspired by triangle strips. By constructing the sequence as a connected chain of faces that explicitly encodes UV boundaries, our method naturally preserves the organized edge flow and semantic layout characteristic of artist-created meshes. A key advantage of this formulation is its unified representation, enabling the same token sequence to be decoded into either a triangle or quadrilateral mesh. This flexibility facilitates joint training on both data types: large-scale triangle data provides fundamental structural priors, while high-quality quad data enhances the geometric regularity of the outputs. Extensive experiments demonstrate that SATO consistently outperforms prior methods in terms of geometric quality, structural coherence, and UV segmentation.
Animatable 3D assets, defined as geometry equipped with an articulated skeleton and skinning weights, are fundamental to interactive graphics, embodied agents, and animation production. While recent 3D generative models can synthesize visually plausible shapes from images, the results are typically static. Obtaining usable rigs via post-hoc auto-rigging is brittle and often produces skeletons that are topologically inconsistent with the generated geometry. We present AniGen, a unified framework that directly generates animate-ready 3D assets conditioned on a single image. Our key insight is to represent shape, skeleton, and skinning as mutually consistent S3 Fields (Shape, Skeleton, Skin) defined over a shared spatial domain. To enable the robust learning of these fields, we introduce two technical innovations: (i) a confidence-decaying skeleton field that explicitly handles the geometric ambiguity of bone prediction at Voronoi boundaries, and (ii) a dual skin feature field that decouples skinning weights from specific joint counts, allowing a fixed-architecture network to predict rigs of arbitrary complexity. Built upon a two-stage flow-matching pipeline, AniGen first synthesizes a sparse structural scaffold and then generates dense geometry and articulation in a structured latent space. Extensive experiments demonstrate that AniGen substantially outperforms state-of-the-art sequential baselines in rig validity and animation quality, generalizing effectively to in-the-wild images across diverse categories including animals, humanoids, and machinery. Homepage: https://yihua7.github.io/AniGen_web/
Despite recent advancements in native latent diffusion models for single-view 3D generation, they still suffer from inadequate geometric details due to the global entanglement of VecSet-based representations and inaccurate structural topology arising from single-view ambiguity. Existing solutions often rely on model scaling over large-scale datasets to improve fidelity, which is computationally prohibitive and inefficient in capturing fine-grained details. In this work, we propose Tempo3D, a resource-efficient paradigm for high-fidelity VecSet-based 3D generation via efficient temporal-aware fine-tuning and multi-view latent aggregation. Our approach consists of two critical components: (1) To facilitate robust detail learning, we establish the TempoDetail dataset and design the Time-Segmented LoRA (TS-LoRA) module. TS-LoRA exploits the inherent "structure-to-details" temporal transition of flow-based models to decouple feature learning, significantly bolstering the capacity to produce high-frequency details without large-scale retraining. (2) To mitigate geometric inaccuracies stemming from single-view ambiguity, we propose a multi-view latent aggregation strategy incorporating Soft Flow Trajectory Projection (Soft FTP) and Score-based Velocity Field Aggregation (SVFA). By aggregating reference views derived from Multimodal Large Language Models (MLLMs), these components resolve the intrinsic conflicts between velocity vectors caused by cross-view latent discrepancies, thereby preventing geometric collapse and ensuring directional consistency. Moreover, the integration of MLLMs empowers Tempo3D to support precise semantic 3D editing via natural language prompts while maintaining structural integrity. Experiments demonstrate that Tempo3D outperforms state-of-the-art baselines in generation quality, geometric precision, and editability under a highly efficient computational budget.
This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained model can be applied to different meshes and triangulations effectively. Practically, the paper adapts the flow matching (FM) paradigm to a mesh-based, triangulation-agnostic setting. Theoretically, it proposes a specific noise distribution which is triangulation agnostic, to be used inside the FM model's denoising process. While noise distributions are usually trivial to devise for, e.g., images, devising a triangulation-agnostic distribution proves to be a much more difficult task. We formulate a mathematical definition of triangulation agnosticism of distributions, via their spectrum. We then show that a discretization of a specific Gaussian random field called a Matérn process holds these desired properties, and provides a simple and efficient sampling algorithm. We use it as our noise model, and adapt FM to the triangulation-agnostic setting by using a state-of-the-art approach for learning signals on meshes in the gradient domain—PoissonNet—as the denoiser. We conduct experiments on elaborate tasks such as sampling elastic rest states, and generating poses of humanoids. Our method is shown to be capable of producing highly realistic results for meshes of over one million triangles, significantly exceeding the state-of-the-art in quality and diversity.
Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization and autoregressive processes, which stuggles in effective inference and is sensitive to error accumulation. In this paper, we present Nexus, a diffusion method that achieves holistic mesh generation via decoupled vertex and topology generation. First, we view mesh vertices as sparse voxels organized as an octree and adopt a diffusion model to generate the vertices in a coarse-to-fine manner. Second, for topology modeling, we propose Space-time Interval, as an extension of Spacetime Distance to encode arbitrary edge and face topology into continuous per-vertex embeddings. It allows for a global and efficient recovery of complex topology. We then employ a diffusion model to generate the continuous embeddings on the generated vertices. Extensive experiments on the Objaverse and Toys4K datasets and in-the-wild images demonstrate that our method outperforms state-of-the-art autoregressive and two-stage baselines, effectively circumventing the inherent limitations of sequential mesh modeling. A blind user study from 3D practitioners confirms strong perceptual preference for our results.
Computer graphics applications are sophisticated heterogeneous programs that orchestrate numerous components, including shaders, pipelines, resource descriptors, and command streams. In modern graphics workflows, communication between these components is mediated by resources such as shader I/O, storage buffers, and resource bindings. To make this communication correct, developers must uphold resource contracts. These contracts cover agreements on type, layout, binding locations, synchronization requirements, and related properties. In standard practice, these contracts are satisfied implicitly through conventions. As a result, contract breaches are often detected too late, at runtime by validation layers, or worse, as visual artifacts in rendered output. In this work, we present RCGP1, a system that mechanistically enforces resource contracts between components of a graphics program. RCGP formalizes contracts, which are centralized through single-source-of-truth resource declarations, as statically introspectable units that can be verified. We map objects from distinct program components to modules that import and export contracts. Modules are then composed into larger components via combinators, which check compatibility between the corresponding contracts and compute the residual contracts for the result. Finally, RCGP upholds these contracts through a set of witnesses, which encode the necessary promises. Using a prototype implementation in C++ and Vulkan, we demonstrate that this formulation converts common classes of late failures, such as descriptor-type mismatches, layout drift, and missing synchronization barriers, into early, actionable compile-time diagnostics.
Synthesizing realistic 3D indoor scenes remains challenging due to data scarcity and the difficulty of simultaneously enforcing global architectural constraints and local semantic consistency. Existing approaches often overlook structural boundaries or rely on fully connected relation graphs that introduce redundant generation errors. Inspired by human design cognition, we present CasLayout, a cascaded diffusion framework that decomposes the joint scene generation task into four conditional sub-stages with explicit physical and semantic roles: (1) predicting furniture quantity and categories, (2) refining object sizes and feature embeddings, (3) modeling spatial relationships in a latent space, and (4) generating Oriented Bounding Boxes (OBBs). This decoupled architecture reduces data requirements and enables flexible integration of Large Language Models (LLMs) and Vision Language Models (VLMs) for zero-shot tasks such as image-to-scene generation. To maintain physical validity within complex floor plans, we explicitly model building elements (e.g., walls, doors, and windows) as conditional constraints. Furthermore, to address the high entropy of dense relation graphs, we introduce a sparse relation graph formulation aligned with human spatial descriptions. By encoding these sparse graphs into a compact latent space using a bidirectional Variational Autoencoder (VAE), the proposed framework provides enhanced relational controllability, allowing generated layouts to better respect functional organization. Experiments demonstrate that CasLayout achieves state-of-the-art performance in fidelity and diversity while enabling improved controllability in practical applications.
We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or multi-view mask aggregation, often suffering from cross-view inconsistency and blurred boundaries, or explore native 3D discriminative segmentation, which typically requires large-scale annotated 3D data and substantial training resources. In contrast, SegviGen leverages the structured priors encoded in pretrained 3D generative model to induce segmentation through distinctive part colorization, establishing a novel and efficient framework for part segmentation. Specifically, SegviGen encodes an input 3D asset and predicts part-indicative colors on active voxels of a geometry-aligned reconstruction. It supports interactive part segmentation, full segmentation, and full segmentation with 2D guidance in a unified framework. Extensive experiments show that SegviGen improves over the prior state of the art by 40% on interactive part segmentation and by 15% on full segmentation, while using only 0.32% of the training data. This undoubtedly demonstrates that pretrained 3D generative priors transfer effectively to 3D part segmentation, enabling strong performance with limited supervision. Code and pretrained weights are publicly available at https://github.com/Nelipot-Lee/SegviGen.
The simultaneous pursuit of high fidelity, large computational throughput, and a minimal memory footprint has long constituted the central challenge in fluid simulation research. Yet state-of-the-art methods struggle to reconcile all these objectives, and often entail navigating trade-offs among them. We present Kinetic Predicted-Moment Flux Reconstruction (KPM-FR), a high-order kinetic-based scheme for low-Mach-number weakly compressible flows that advances all three fronts within a single framework. KPM-FR is a flux-form fluid flow solver rooted in the principles of the gas-kinetic scheme (GKS), deriving numerical fluxes from the locally evolved Boltzmann-BGK equation to recover Navier-Stokes (NS) solutions. Departing from the GKS and its variants, it carries out kinetic evolution entirely in moment space within the high-order flux reconstruction (FR) framework through a concise predictor-corrector scheme. This translates to two fused GPU kernels per time step, streamlining computation and confining intermediate data to on-chip memory. This design confers several practical advantages. First, compared to conventional high-fidelity lattice Boltzmann methods (LBM), the moment-based formulation reduces the per-point memory footprint by more than fivefold. Second, at matched resolutions, its high-order spatial formulation exhibits markedly lower numerical dissipation, preserving fine-scale vortical structures with greater fidelity. Combining these advantages with a near-saturated throughput exceeding 8 billion solution-point updates per second on a single consumer GPU, KPM-FR delivers large-scale fluid simulation on commodity hardware. Quantitative benchmarks confirm highorder spatial convergence and spectral-like dissipation characteristics, while validation against reference data for flow past solid bodies verifies its practical accuracy. Ultimately, we demonstrate the versatility of KPM-FR across complex geometries and large-scale turbulent flows, capturing multiscale structures with 1.8 billion solution points on a single desktop workstation.
This paper presents a highly efficient and robust differentiable flu::id framework, centered on a novel surrogate gradient method that utilizes the flow map structural advantages. Our key insight reveals a significant misalignment between computational intensity and gradient importance during the backward pass. Specifically, we identify a physical duality within the adjoint process, revealing that the cross-step connections inherent in the flow map act as dominant gradient "highways" that propagate sensitivities over long horizons with high fidelity. Leveraging these insights, we develop a surrogate gradient model that retains these critical connections while pruning redundant adjoint computations in a physics-informed manner. Integrated with tailored acceleration techniques, our framework is successfully applied to diverse, challenging optimization tasks characterized by long time horizons and rich vorticity. Results demonstrate significant speedups and memory reductions while maintaining nearly-identical gradients compared to the full-gradient baseline.