ALT We present GaussianSpline, a dynamic 4D Gaussian Splatting method that models per-Gaussian motion using adaptive, non-uniform piecewise cubic Bézier splines. Unlike approaches based on global deformations, fixed-order polynomials, or uniformly parameterized splines, GaussianSpline jointly learns the Bézier control points and the temporal partition of each Gaussian's trajectory. This adaptive representation allocates greater temporal capacity to highly dynamic regions while maintaining compact trajectories for near-static regions, enabling high-fidelity reconstruction of complex and rapidly changing motion. To jointly achieve globally smooth motion and local adaptivity, we enforce G¹ continuity between consecutive Bézier segments using a reflection-based constraint on their control points. This produces smooth, fully differentiable trajectories while preserving the flexibility of non-uniform temporal parameterization. Experiments on the Neural 3D Video benchmark, the Google Immersive da

ALT Reconstructing and rendering high-fidelity, animatable, and try-on-ready virtual avatars using only a single camera is crucial for many applications. However, existing try-on-ready human avatar reconstruction methods struggle to achieve this from monocular videos or single images, primarily due to two issues: (1) existing approaches typically use a single layer to represent both garments and the underlying body, ignoring their distinct motion characteristics; and (2) input captured from a single camera often fails to fully cover both the garments and the underlying body. To address these challenges, we propose a physically integrated, double-layer GS-Mesh representation for reconstructing decoupled 3D clothed human avatars from monocular videos or even single images by incorporating 2D generated data. While 2D generation methods can produce video sequences from given pose sequences, they struggle to maintain inter-frame consistency. We decompose the inter-frame inconsistencies in the g

ALT Body deformation caused by muscle contraction plays a vital role in the visual realism of character animation, yet in most 3D animation pipelines artists have to manually specify muscle activations, which is time-consuming and gives no guarantee of physical realism. To address this, we propose a new method in which a reinforcement learning (RL) controller discovers per-muscle activations that track reference motions without manual tuning. Existing RL muscle controllers stop at 1D muscles; we go one step further, keeping the 1D model inside the RL control loop as a fast surrogate, and using a 3D volumetric simulation to produce the final deformation. Because both stages share the very same constitutive law, the muscle deformation is produced by the same activations that generated the motion. We further add a measured electromyography (EMG) prior that steers the redundant activation solution toward real human muscle recruitment. To achieve stable, efficient, and realistic simulation, we

ALT Reasoning about fine-grained placement constraints, such as support and containment, is essential for realistic 3D scene creation. It remains a significant challenge for existing methods that rely on simplified object representations like oriented bounding box (OBB), which are blind to the detailed geometry. To address this limitation, we introduce GALA, a geometry-aware language model with explicit point cloud perception for controllable object placement. Our model performs autoregressively by predicting the 6D pose of one target object at a time conditioned on the scene context and placement instruction. We utilize a two-stage training strategy: the first stage aligns geometric and textual modalities through pretraining tasks, and the second stage fine-tunes the model for placement. To facilitate this task, we also curate a multi-source scene dataset covering diverse room types and fine-grained furniture arrangements, which will be open-sourced for community benefit. Experiments on o

ALT Sprite-based animation remains prevalent in video game development, but production workflows require capabilities beyond traditional in-betweening. While existing neural interpolation methods can generate intermediate frames, animators also need forward extrapolation to predict motion continuations and backward extrapolation for editing and reverse-engineering existing sprite sheets. Current video generation models either operate on natural video content with stochastic diffusion sampling incompatible with production requirements, or focus exclusively on interpolation without addressing extrapolation tasks. We present a deep learning approach for controlled directional frame generation in sprite animation using three specialized expert models that share identical U-Net encoder-decoder architectures and several domain-specific modifications, including RGBA-aware processing, multi-scale flow refinement, pixel-shuffle upsampling, and alpha-gated blending. Each model is trained on differen

ALT Branching Walk-on-Spheres (WoS) solves semilinear PDEs without meshes, but its computational cost grows sharply near critical branching rates. We introduce Walk in Envelopes (WiE), which uses certified solution bounds to transform branching diffusion rather than prune existing trees. The transformation suppresses reproduction, introduces controlled termination, and preserves unbiasedness with an envelope-dependent variance bound. We realize the transformed process through exact rejection sampling using standard screened WoS kernels, supported by certified spline envelopes and adaptive sampling, and further prove that suitable harmonic envelopes yield bounded expected reproduction counts even as conventional BBM diverges.

ALT B-spline lofting through closed cross-sections is common in reverse engineering, but interpolating each section on its own knot vector can produce an unnecessarily large control net. We construct a common-knot method for closed section curves of odd and even degree by minimizing the strain energy of each section subject to its interpolation constraints. Feasibility for closed section data and uniqueness of the resulting saddle-point solution require full row rank of the periodic collocation matrices, a property that a previously proposed periodic Schoenberg-Whitney window condition does not always guarantee. We establish the exact parity distinction: the condition is sufficient at odd point counts but admits singular configurations at every admissible even point count. For the odd and even degrees used here, the natural and shifting anchor knots are nonsingular; at odd point counts the window condition certifies the selected candidate, whereas at even point counts a rank test with an a

ALT Lattice inverse-design methods can generate cells that match prescribed effective properties, but property feasibility does not imply material efficiency. Removing redundant material from an existing lattice while preserving its homogenized elasticity ordinarily requires solving a separate constrained topology optimization problem for each cell, making large-scale refinement computationally expensive. We introduce SLIM (Structural Lightweighting via Implicit Mapping), a neural lattice-refinement model that replaces this per-instance iterative optimization with a single forward prediction. SLIM uses a dual-octree implicit network to predict the signed-distance field of a lightweight lattice and is trained on input–output pairs generated by property-constrained topology optimization. To improve stiffness preservation, we use homogenization sensitivities computed on the optimized target to estimate how residual voxel errors in a prediction affect its homogenized stiffness, and penalize th

ALT Recent advances in general-purpose AI models and agentic systems have enabled them to perform some CAD tasks. A systematic assessment is now needed to establish what these systems can accomplish and how well their outputs meet engineering requirements. This paper presents an empirical study of GPT-6 Astra, a SOTA general-purpose AI model, across four basic CAD tasks: model understanding, generation, editing, and evaluation. We design test cases spanning different levels of complexity and engineering requirements. The results show that GPT-6 Astra can support multiple stages of CAD workflows, with successful outcomes in cases involving nontrivial geometric complexity. This suggests that general-purpose AI is beginning to offer practical value in almost all steps in CAD workflow. However, gaps remain between generating visually plausible geometry and producing valid, functional, and editable CAD models. Recurring errors in freeform geometry, parameter dependencies, and mating relationshi

ALT Residual stress remains in an object even in the absence of external forces or thermal pressure, which, in turn, may cause significant plastic deformations. In case the residual stress creates unwanted effects on the material and so is undesirable, an efficient solution is necessary to track and eliminate this stress. Smoothed Particle Hydrodynamics has been extensively used in solid mechanics simulations and the inherent colour-field generation approach is a promising tracker for the residual stress. In this paper, we propose a way to use the colour-field approach for eliminating the residual stress and prevent the undesirable premature failure of solid objects.