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首次观测 · 记录 #1CVF · CVPR 2026:cvf:05033f52dd86af28df8812e8c971797dCVF · CVPR 2026

CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance

Wang, Hanyang, Liu, Yiyang, Chi, Jiawei, Liu, Fangfu, Xue, Ran, Duan, Yueqi

原文摘要

Classifier-Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow-based diffusion models. In this paper, we explore a unified framework called **CFG-Ctrl**, which reinterprets CFG as a control applied to the first-order continuous-time generative flow, using the conditional-unconditional discrepancy as an error signal to adjust the velocity field. From this perspective, we summarize vanilla CFG as a proportional controller (P-control) with fixed gain, and typical follow-up variants develop extended control-law designs derived from it. However, existing methods mainly rely on linear control, inherently leading to instability, overshooting, and degraded semantic fidelity especially on large guidance scales. To address this, we introduce Sliding Mode Control CFG (**SMC-CFG**), which enforces the generative flow toward a rapidly convergent sliding manifold. Specifically, we define an exponential sliding mode surface over the semantic prediction error and introduce a switching control term to establish nonlinear feedback-guided correction. Moreover, we provide a Lyapunov stability analysis to theoretically support finite-time convergence. Experiments across text-to-image generation models including Stable Diffusion 3.5, Flux, and Qwen-Image demonstrate that SMC-CFG outperforms standard CFG in semantic alignment and enhances robustness across a wide range of guidance scales.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1288dc7668b1e9a2dcf73c9514ab7238CVF · CVPR 2026

SimRecon: SimReady Compositional Scene Reconstruction from Real Videos

Xia, Chong, Zhu, Kai, Wang, Zizhuo, Liu, Fangfu, Zhang, Zhizheng, Duan, Yueqi

原文摘要

Compositional scene reconstruction seeks to create object-centric representations rather than holistic scenes from real-world videos, which is natively applicable for simulation and interaction. Conventional compositional reconstruction approaches primarily emphasize on visual appearance and show limited generalization ability to real-world scenarios. In this paper, we propose SimRecon, a framework that realizes a "Perception-Generation-Simulation" pipeline towards cluttered scene reconstruction, which first conducts scene-level semantic reconstruction from video input, then performs single-object generation, and finally assembles these assets in the simulator. However, naively combining these three stages leads to visual infidelity of generated assets and physical implausibility of the final scene, a problem particularly severe for complex scenes. Thus, we further propose two bridging modules between the three stages to address this problem. To be specific, for the transition from Perception to Generation, critical for visual fidelity, we introduce Active Viewpoint Optimization, which actively searches in 3D space to acquire optimal projected images as conditions for single-object completion. Moreover, for the transition from Generation to Simulation, essential for physical plausibility, we propose a Scene Graph Synthesizer, which guides the construction from scratch in 3D simulators, mirroring the native, constructive principle of the real world. Extensive experiments on the ScanNet dataset validate our method's superior performance over previous state-of-the-art approaches.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:15205bf760acf7393078d8af917bc923CVF · CVPR 2026

StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References

He, Boyu, Ye, Yunfan, Liu, Chang, Wu, Weishang, Liu, Fang, Cai, Zhiping

原文摘要

Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics, causing uncontrollable stylization; 2) reliance on extra constraints (e.g., semantic masks) restricting applicability; 3) rigid feature associations lacking adaptive global-local alignment, failing to balance fine-grained stylization and global content preservation. These limitations, particularly the inability to flexibly leverage style inputs, fundamentally restrict style transfer in terms of personalization, accuracy, and adaptability.To address these, we propose StyleGallery, a training-free and semantic-aware framework that supports arbitrary reference images as input and enables effective personalized customization.It comprises three core stages: semantic region segmentation (adaptive clustering on latent diffusion features to divide regions without extra inputs); clustered region matching (block filtering on extracted features for precise alignment); and style transfer optimization (energy function-guided diffusion sampling with regional style loss to optimize stylization).Experiments on our introduced benchmark demonstrate that StyleGallery outperforms state-of-the-art methods in content structure preservation, regional stylization, interpretability, and personalized customization, particularly when leveraging multiple style references. The source code and dataset are available at https://github.com/iiiiiiiword/StyleGallery.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:2a671f6a0aebbadb4277bf3d1d5a8aeaCVF · CVPR 2026

CLEX: Complementary Label Exchange Learning for Noisy Facial Expression Recognition

Wang, Lin, Liu, Fang, Xing, Xiaofen, Guo, Kailing, Xu, Xiangmin

原文摘要

Facial expression recognition (FER) in the wild is severely hampered by label noise and annotation ambiguity. Existing methods, including sample selection, label ensembling, and consistency regularization, primarily rely on ordinary label supervision and offer limited control over non-target predictions, leading to spurious activations and overfitting to noisy labels. To address this limitation, we propose a novel learning framework, named Complementary Label Exchange Learning (CLEX), to enhance robustness by exchanging knowledge from non-target predictions across augmented views. Specifically, CLEX comprises three synergistic components. First, Stochastic Non-Target Logit Exchange randomly swaps a subset of non-target logits between original and augmented views to couple error-prone predictions, creating robust consistency constraints. Second, Scale-Invariant Logit Normalization eliminates magnitude artifacts through Lp-norm normalization, ensuring that regularization operates over geometrically meaningful directions rather than being dominated by arbitrary scales. Third, Complementary Suppression Loss selectively penalizes spurious activations over a randomly retained subset of non-target classes, avoiding the uniform shrinkage that hampers discriminative learning. To further stabilize training, we incorporate attention consistency regularization that enforces spatial alignment between augmented views, while retaining auxiliary cross-entropy to preserve semantic localization capability. Extensive experiments across multiple benchmark FER datasets (RAF-DB, FERPlus, and AffectNet) demonstrate that CLEX consistently outperforms existing robust FER learning approaches.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:2a75940ac91223a728c6b98cc6affafcCVF · CVPR 2026

Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control

Li, Zhe, Chi, Cheng, Wei, Yangyang, Zhu, Boan, Huang, Tao, Sun, Zhenguo, Peng, Yibo, Wang, Pengwei 等 12 位作者

原文摘要

Humans intuitively move to sound, but current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands. Generating motion from audio and then retargeting it to robots relies on explicit motion reconstruction, leading to cascaded errors, high latency, and disjointed acoustic-actuation mapping. We propose RoboPerform, the first unified audio-to-locomotion framework that can directly generate music-driven dance and speech-driven co-speech gestures from audio. Guided by the core principle of "motion = content + style", the framework treats audio as implicit style signals and eliminates the need for explicit motion reconstruction. RoboPerform integrates a ResMoE teacher policy for adapting to diverse motion patterns and a diffusion-based student policy for audio style injection. This retargeting-free design ensures low latency and high fidelity. Experimental validation shows that RoboPerform achieves promising results in physical plausibility and audio alignment, successfully transforming robots into responsive freestyle performers capable of reacting to audio.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:3efdd67a3c1a6b556dfabff44991fc35CVF · CVPR 2026

SketchRevive: Fine-Grained Pixel-to-Vector Sketch Completion with Diffusion-Prior-Guided Multimodal LLMs

Zuo, Ran, Hu, Haoxiang, Pei, Chenxi, Liu, Yanxuan, Qiang, Wenwen, Liu, Fang, Deng, Xiaoming, Ma, Cuixia 等 9 位作者

原文摘要

Transforming sparse, partial pixel sketches from diverse media into complete, editable vector drawings is essential yet underexplored in digital creation. Prior methods either generate from scratch or inpaint local gaps without predicting global structure, leading to coarse contours and limited detail. To address this, we introduce SketchRevive, a two-stage framework for fine-grained pixel-to-vector sketch completion that couples diffusion-based pixel completion with MLLM-driven refinement and vectorization to produce coherent, detail-faithful SVG results. Specifically, we first construct a practical benchmark by augmenting stroke-annotated sketches from paper and whiteboards. Stage I trains a diffusion model with a line-distribution head to predict per-pixel stroke presence, producing structurally and visually consistent completions. Stage II fine-tunes an MLLM for structure-aware SVG vectorization with iterative refinement, optimized by instance-level stroke attribute similarities. To align key clues, e.g., spatial structure, appearance details across both stages, we introduce a diffusion-prior aggregated encoding module by injecting multi-scale UNet features from Stage I into the MLLM's visual embeddings and using line prediction logits for token compression to prioritize informative tokens. Experiments indicate that SketchRevive completes topology-coherent vector outputs with high fidelity and recognizability while preserving user intent, suitable for interactive creation and artistic design.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9220f8fb7ade12f42502b881e5cecb5fCVF · CVPR 2026

CoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image Generation

Song, Yuxin, Lu, Yu, Sun, Haoyuan, Yao, Huanjin, Liu, Fanglong, Sun, Yifan, Feng, Haocheng, Zhou, Hang 等 9 位作者

原文摘要

Unified conditional image generation remains difficult because different tasks depend on fundamentally different internal representations. Some require conceptual understanding for semantic synthesis, while others rely on localization cues for spatial precision. Forcing these heterogeneous tasks to share a single representation leads to concept-localization representational conflict.To address this issue, we propose CoLoGen, a unified diffusion framework that progressively learns and reconciles this concept-localization duality. CoLoGen uses a staged curriculum that first builds core conceptual and localization abilities, then adapts them to diverse visual conditions, and finally refines their synergy for complex instruction-driven tasks. Central to this process is the Progressive Representation Weaving (PRW) module, which dynamically routes features to specialized experts and stably integrates their outputs across stages.Experiments on editing, controllable generation, and customized generation show that CoLoGen achieves competitive or superior performance, offering a principled representational perspective for unified image generation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:98a6b4b960151313927ad87d0c772f19CVF · CVPR 2026

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models

Li, Ruiyang, Liu, Fang, Jiao, Licheng, Xie, Xinglin, Hao, Jiayao, Li, Shuo, Liu, Xu, Yang, Jingyi 等 11 位作者

原文摘要

Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medical image datasets suffer from acquisition noise and annotation ambiguity, causing pervasive data uncertainty that substantially undermines model robustness. Existing research focuses primarily on model architectural improvements and predictive reliability estimation, while systematic exploration of the intrinsic data uncertainty remains insufficient. To address this gap, this work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty. Specifically, we analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class, thereby measuring sample difficulty and aleatoric uncertainty. Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability. Experimental results on five public datasets encompassing CT and MRI modalities and involving multi-organ and tumor segmentation tasks demonstrate that our method achieves significant and robust performance improvements across various mainstream network architectures, revealing the broad application potential of aleatoric uncertainty in medical image understanding and segmentation tasks. The code is available.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ab3ccab2fcb96327fc1fb41900837ee8CVF · CVPR 2026

GenSplat: Bridging the Generalization Gap in 3DGS Language Comprehension

Liu, Fang, Liu, Yuhao, Xu, Ke, Hancke, Gerhard Petrus, Lau, Rynson W. H.

原文摘要

In this paper, we propose GenSplat, a novel approach for language comprehension in 3D Gaussian Splatting (3DGS). Unlike previous methods that either achieve cross-scene generalization by being bounded to a predefined vocabulary or handle free-form language by overfitting to individual scenes, GenSplat is robust to free-form language queries and generalizable across 3DGS scene representations. Our key insight for this problem is to formulate a structured learning process to progressively align linguistic concepts with 3D Gaussians. It contains two novel technical contributions. First, we propose a Progressive Language Grounding Curriculum that structurally guides the model through learning category-level semantics to instance-level concepts and free-form language, preventing overfitting by building a generalizable language feature space. Second, we design a Multi-modal Large Language Models (MLLM)-guided Reasoning Module that leverages MLLM's semantic and spatial priors to enhance 3D localization and reasoning. To further improve spatial alignment and computational efficiency, we introduce a GeometryAware Frame Selector (GAFS), which adaptively selects the most informative views based on Gaussian and textural cues. Extensive cross-task evaluations (including 3D referring segmentation, 3D visual question answering, and 3D open-vocabulary understanding) demonstrate state-of-the-art performances and strong generalization capability of GenSplat. We will release the codes.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:aebb27817af951013424bb5764098943CVF · CVPR 2026

VDFE: Difference-Aware 3D Scene Editing with Non-Intrusive Video Diffusion Priors for Multi-View Consistency and Efficiency

Zhang, Chao, Liu, Fang, Li, Shuo, Liu, Yang, Wang, Jiahao, Huang, Xinyan, Li, Lingling, Chen, Puhua 等 11 位作者

原文摘要

Text-driven 3D editing, enabled by advancements in 3D reconstruction techniques such as NeRF and 3D Gaussian Splatting, aims to provide intuitive scene customization. However, existing methods frequently exhibit limitations in controllability and consistency. To address these shortcomings, we propose VDFE, a difference-aware 3D scene editing method based on non-intrusive utilization of pre-trained video diffusion priors, which integrates Optimal Control Guided Flow Editing (FlowOCE), Decoupled Flow Difference (DFD), and Difference-Aware Gaussians Editing (DAGE) modules. Specifically, FlowOCE treats the editing process as an optimal control problem, optimizing a noise-free editing trajectory to minimize unintended modifications in non-target regions and generating results with multi-view consistency and smooth transitions; DFD innovatively achieves more precise localization of editing regions compared to cross-attention mechanisms by analyzing flow differences, which supplies priors for the subsequent optimization process; and DAGE leverages precise localization to selectively update 3D Gaussians for efficient and precise refinement. Extensive experiments demonstrate that our method significantly outperforms existing methods in both qualitative and quantitative evaluations, achieving state-of-the-art (SOTA) performance.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗