Existing mobile devices are constrained by compact optical designs, such as small apertures, which make it difficult to produce natural, optically realistic bokeh effects. Although recent learning-based methods have shown promising results, they still struggle with photos captured under high digital zoom levels, which often suffer from reduced resolution and loss of fine details. A naive solution is to enhance image quality before applying bokeh rendering, yet this two-stage pipeline reduces efficiency and introduces unnecessary error accumulation. To overcome these limitations, we propose MagicBokeh, a unified diffusion-based framework designed for high-quality and efficient bokeh rendering. Through an alternative training strategy and a focus-aware masked attention mechanism, our method jointly optimizes bokeh rendering and super-resolution, substantially improving both controllability and visual fidelity. Furthermore, we introduce degradation-aware depth module to enable more accurate depth estimation from low-quality inputs. Experimental results demonstrate that MagicBokeh efficiently produces photorealistic bokeh effects, particularly on real-world low-resolution images, paving the way for future advancements in bokeh rendering. Our code and models are available at this \href https://github.com/vivoCameraResearch/MagicBokeh url .
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
In-Context Learning (ICL) has shown great effectiveness in developing generalist image segmentation models. Its significant advantage over text-based descriptions is the ability to convey intricate visual appearance details through simple reference images. However, finding a perfectly matching single example for real-world rare and complex concepts is difficult. Moreover, existing methods are largely confined to semantic or instance-level understanding of the reference image, struggling to express more precise segmentation needs through the input. To address this, we propose CDICS, a novel framework that leverages Compositional prompts and phased task Decoupling to achieve compositional prompt-controlled In-Context Segmentation. Our method introduces compositional prompts derived from reference prompts, combining semantic, part and color images to dynamically define segmentation targets. To effectively fuse this control information, ensure synergy while suppressing interference, and mitigate feature coupling risks, our decoupled two-stage architecture firstly performs coarse-grained semantic localization, then refines the result using compositional appearance prompts to precisely match the specified attributes. This design extends traditional in-context segmentation, enabling it to support compositional prompts. Additionally, we reconstructed two datasets and their benchmarks to acquire data with part-color-specific attributes. Our method demonstrates superior performance on the compositional prompt-controlled in-context segmentation task. It also extends the capabilities of existing in-context segmentation, and makes an attempt toward real-world fine-grained segmentation.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Li, Shiyao, Guédon, Antoine, Chen, Shizhe, Lepetit, Vincent
Active mapping aims to determine how an agent should move to efficiently reconstruct an unknown environment. Most existing approaches rely on greedy next-best-view prediction, resulting in inefficient exploration and incomplete scene reconstruction. To address this limitation, we introduce MAGICIAN, a novel long-term planning framework that maximizes accumulated surface coverage gain through Imagined Gaussians, a scene representation derived from a pre-trained occupancy network with strong structural priors. This representation enables efficient computation of coverage gain for any novel viewpoint via fast volumetric rendering, allowing its integration into a tree-search algorithm for long-horizon planning. We update Imagined Gaussians and refine the planned trajectory in a closed-loop manner. Our method achieves state-of-the-art performance across indoor and outdoor benchmarks with varying action spaces, demonstrating the critical advantage of long-term planning in active mapping.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Distinct from attention-based compression methods, this paper presents an information uniqueness driven video compression framework, termed UniComp, which aims to maximize the information fidelity of video representations under constrained computational budgets. Starting from the information-theoretic perspective, we formulate the vision compression as an optimization problem that minimizes conditional entropy (reconstruction error) between retained and full tokens. To achieve this, we introduce the notion of information uniqueness to measure intrinsic redundancy among tokens to link with reconstruction error. Based on uniqueness, we design three modules--Frame Group Fusion, Token Allocation, and Spatial Dynamic Compression--that progressively perform semantic frame grouping, adaptive resource allocation, and fine-grained spatial compression. Extensive experiments demonstrate that UniComp consistently outperforms existing compression methods in preserving essential visual tokens under limited computational budgets, highlighting the pivotal role of information uniqueness in token compression efficacy.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Chen, Shifeng, Li, Yihui, Liao, Jun, Yang, Hongyu, Huang, Di
Recent advances in 3D scene editing using NeRF and 3DGS enable high-quality static scene editing. In contrast, dynamic scene editing remains challenging, as methods that directly extend 2D diffusion models to 4D often produce motion artifacts, temporal flickering, and inconsistent style propagation. We introduce Catalyst4D, a framework that transfers high-quality 3D edits to dynamic 4D Gaussian scenes while maintaining spatial and temporal coherence. At its core, Anchor-based Motion Guidance (AMG) builds a set of structurally stable and spatially representative anchors from both original and edited Gaussians. These anchors serve as robust region-level references, and their correspondences are established via optimal transport to enable consistent deformation propagation without cross-region interference or motion drift. Complementarily, Color Uncertainty-guided Appearance Refinement (CUAR) preserves temporal appearance consistency by estimating per-Gaussian color uncertainty and selectively refining regions prone to occlusion-induced artifacts. Extensive experiments demonstrate that Catalyst4D achieves temporally stable, high-fidelity dynamic scene editing and outperforms existing methods in both visual quality and motion coherence.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Modern generative models can propose striking 3D vehicle shapes from text and images, but turning these sketches into aerodynamically efficient, regulation compliant designs still requires weeks of high-fidelity computational fluid dynamics (CFD) and manual iteration. As a result, fast 3D generation without trustworthy physics in the loop does little to reduce end-to-end design time. We study how an AI agent can close this loop under a strict CFD budget. We introduce AeroAgent, a vision-physics-decision framework built around a single 3D, editable surface representation for vehicle shapes. A vision module turns text and 2D references into diverse, standardized 3D candidates and supports image-level edits. A physics module, AeroFormer is a geometry-guided Transformer surrogate trained on a large-scale vehicle aerodynamics dataset of roughly 50k CFD simulations; three task-specific heads predict drag Cd, surface pressure, and velocity fields on shared 3D grids. A decision module encodes regulatory size limits and aesthetic constraints as feasibility tests, uses prototype priors and surrogate sensitivities to guide free-form deformation edits, and runs a budget-aware propose evaluate refine loop in which only the final top K shapes are confirmed by high-fidelity CFD. In extensive experiments across five common vehicle classes, running only five propose evaluate refine iterations per vehicle reduces drag by an average of 2-12% and cuts high-fidelity CFD calls by 50-80% compared to baseline workflows, while preserving or improving styling quality.
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cs.CV · cs.AI
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Xiang, Wei, Wu, Yexinrui, Chen, Xinli, Li, Xinran, Chen, Shi
User Interface (UI) display defect detection poses challenges far beyond UI understanding, requiring fine-grained element boundary understanding, missing-content detection, and reasoning about sequential interface semantic consistency. However, the capabilities of multimodal large language models (MLLMs) and vision-language models (VLMs) for detecting UI defects in realistic, complex interfaces have not been systematically validated. To fill this gap, we present UI-Lens, the first multi-dimensional UI display detection benchmark for Chinese-language UI scenarios. The dataset comprises 4,759 pages meticulously annotated by design experts, covering six core display defect categories. We conduct a systematic evaluation of 10 mainstream models (8 closed-source, 2 open-source). Results show clear shortcomings in current models: for tasks requiring fine-grained element boundary understanding, performance is near random, with task-average F1 scores of 20.36% and 31.21% on Text Overflow and Container Overlap, respectively; for sequential interface semantic consistency (e.g., Text Inconsistency), the task-average F1 score is only 10.61%, indicating severe underperformance. We release UI-Lens to catalyze research toward more robust UI display defect detection with fine-grained boundary awareness in realistic, complex interfaces.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表