Current parameterized image representations embed visual information along the semantic boundaries and struggle to express the internal detailed texture structures of image components, leading to a lack of content consistency after image editing and driving. To address these challenges, this work proposes a novel parameterized representation based on hierarchical image proxy geometry, utilizing multi-layer hierarchically interrelated proxy geometric control points to embed multi-scale long-range structures and fine-grained texture details. The proposed representation enables smoother and more continuous interpolation during image rendering and ensures high-quality consistency within image components during image editing. Additionally, under the layer-wise representation strategy based on semantic-aware image layer decomposition, we enable decoupled image shape/texture editing of the targets of interest within the image. Extensive experimental results on image vectorization and editing tasks demonstrate that our proposed method achieves high rendering accuracy of general images, including natural images, with a significantly higher image parameter compression ratio, facilitating user-friendly editing of image semantic components.

Accurately and efficiently simulating complex fluid dynamics is a challenging task that has traditionally relied on computationally intensive methods. Neural network-based approaches, such as convolutional and graph neural networks, have partially alleviated this burden by enabling efficient local feature extraction. However, they struggle to capture long-range dependencies due to limited receptive fields, and Transformer-based models, while providing global context, incur prohibitive computational costs. To tackle these challenges, we propose AMR-Transformer, an efficient and accurate neural CFD-solving pipeline that integrates a novel adaptive mesh refinement scheme with a Navier-Stokes constraint-aware fast pruning module. This design encourages long-range interactions between simulation cells and facilitates the modeling of global fluid wave patterns, such as turbulence and shockwaves. Experiments show that our approach achieves significant gains in efficiency while preserving critical details, making it suitable for high-resolution physical simulations with long-range dependencies. On CFDBench, PDEBench and a new shock wave dataset, our pipeline demonstrates up to an order-of- magnitude improvement in accuracy over baseline models. Additionally, compared to ViT, our approach achieves a reduction in FLOPs of up to 60 times.

We introduce the AR2 O Painter, an interactive intelligent system designed for real-time, highly realistic oil painting creation. This Agent can faithfully reproduce any portrait image, allowing users to visually enjoy the stroke-by-stroke painting process immersively as the artwork is completed within two minutes. It consists of two modules: the Oil Painting Stroke Sequence Planner, which performs multi-level semantic-based brushstroke sequence decomposition on portrait images, mimicking the logic of artist painting, and the Oil Painting Rendering Engine, which receives the brushstroke sequence, models the pigment via fluid dynamics, simulates its interaction with the canvas and brush, and applies a tailored PBR model with microfacet BRDF, Fresnel effects, and stroke-level geometry, enabling perceptually plausible gloss and fine-grained surface relief. To the best of our knowledge, it is the first real-time intelligent painting system to generate realistic oil paintings with high interactivity and artistic fidelity. The demo video is available at https://youtu.be/aN-W06GmnP8.

Intelligent generation of vector graphics has very promising applications in the fields of advertising and logo design, artistic painting, animation production, etc. However, current mainstream vector image generation methods lack the encoding of image appearance information that is associated with the original vector representation and therefore lose valid supervision signal from the strong correlation between the discrete vector parameter (drawing in-struction) sequence and the target shape/structure of the corresponding pixel image. On the one hand, the gener-ation process based on pure vector domain completely ignores the similarity measurement between shape parameter (and their combination) and the paired pixel image appearance pattern; on the other hand, two-stage methods (i.e., generation-and-vectorization) based on pixel diffusion followed by differentiable image-to-vector translation suf-fer from wrong error-correction signal caused by approxi-mate gradients. To address the above issues, we propose a novel generation framework based on dual-domain (vector-pixel) diffusion with cross-modality impulse signals from each other. First, in each diffusion step, the current representation extracted from the other domain is used as a condition variable to constrain the subsequent sampling operation, yielding shape-aware new parameterizations; second, independent supervision signals from both domains avoid the gradient error accumulation problem caused by cross-domain representation conversion. Extensive experimental results on popular benchmarks including font and icon datasets demonstrate the great advantages of our proposed framework in terms of generated shape quality.

This work explores a novel image geometric abstraction paradigm based on assembly out of a pool of pre-defined simple parametric primitives (i.e., triangle, rectangle, circle and semicircle), facilitating controllable shape editing in images. While cast as a mixed combinatorial and continuous optimization problem, the above task is approximately reformulated within a token translation neural framework that simultaneously outputs primitive assignments and corresponding transformation and color parameters in an image-to-set manner, thus bypassing complex/non-differentiable graph-matching iterations. To relax the searching space and address the vanishing gradient issue, a novel Neural Soft Assignment scheme that well explores the quasi-equivalence between the assignment in Bipartite b-Matching and opacity-aware weighted multiple rasterization combination is introduced, drastically reducing the optimization complexity. Without ground-truth image abstraction labeling (i.e., vectorized representation), the whole pipeline is end-to-end trainable in a self-supervised manner, based on the linkage of differentiable rasterization techniques. Extensive experiments on several datasets well demonstrate that our framework is able to predict highly compelling vectorized geometric abstraction results with a combination of ONLY four simple primitives, also with VERY straightforward shape editing capability by simple replacement of primitive type, compared to previous image abstraction and image vectorization methods.