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首次观测 · 记录 #1CVF · CVPR 2026:cvf:07b2570281788d7b11ff96bea8007b97CVF · CVPR 2026

SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian Splatting

Li, Bo, Kang, Jiahao, Ma, Yubo, Liu, Feng-Lin, Liu, Bin, Zhang, Fang-Lue, Gao, Lin

原文摘要

3D Gaussian representations have emerged as a powerful paradigm for digital head modeling, achieving photorealistic quality with real-time rendering. However, intuitive and interactive creation or editing of 3D Gaussian head models remains challenging. Although 2D sketches provide an ideal interaction modality for fast, intuitive conceptual design, they are sparse, depth-ambiguous, and lack high-frequency appearance cues, making it difficult to infer dense, geometrically consistent 3D Gaussian structures from strokes--especially under real-time constraints. To address these challenges, we propose SketchFaceGS, the first sketch-driven framework for real-time generation and editing of photorealistic 3D Gaussian head models from 2D sketches. Our method uses a feed-forward, coarse-to-fine architecture. A Transformer-based UV feature-prediction module first reconstructs a coarse but geometrically consistent UV feature map from the input sketch, and then a 3D UV feature enhancement module refines it with high-frequency, photorealistic detail to produce a high-fidelity 3D head. For editing, we introduce a UV Mask Fusion technique combined with a layer-by-layer feature-fusion strategy, enabling precise, real-time, free-viewpoint modifications. Extensive experiments show that SketchFaceGS outperforms existing methods in both generation fidelity and editing flexibility, producing high-quality, editable 3D heads from sketches in a single forward pass.

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:0a1ed9cc733d60f0043c1016ede976c8CVF · CVPR 2026

Neural Distribution Prior for LiDAR Out-of-Distribution Detection

Li, Zizhao, Xiang, Zhengkang, Ao, Jiayang, Liu, Feng, West, Joseph, Khoshelham, Kourosh

原文摘要

LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operate under the closed-set assumption and often fail to recognize unexpected out-of-distribution (OOD) objects in the open world. Existing OOD scoring functions exhibit limited performance because they ignore the pronounced class imbalance inherent in LiDAR OOD detection and assume a uniform class distribution. To address this limitation, we propose the Neural Distribution Prior (NDP), a framework that models the distributional structure of network predictions and adaptively reweights OOD scores based on alignment with a learned distribution prior. NDP dynamically captures the logit distribution patterns of training data and corrects class-dependent confidence bias through an attention-based module. We further introduce a Perlin noise-based OOD synthesis strategy that generates diverse auxiliary OOD samples from input scans, enabling robust OOD training without external datasets. Extensive experiments on the SemanticKITTI and STU benchmarks demonstrate that NDP substantially improves OOD detection performance, achieving a point-level AP of 61.31% on the STU test set, which is more than 10xhigher than the previous best result. Our framework is compatible with various existing OOD scoring formulations, providing an effective solution for open-world LiDAR perception.

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:185f917d3116caab10acb0225387b3b7CVF · CVPR 2026

A Unified Perspective on Adversarial Membership Manipulation in Vision Models

Gao, Ruize, Zhou, Kaiwen, Chen, Yongqiang, Liu, Feng

原文摘要

Membership inference attacks (MIAs) aim to determine whether a specific data point was part of a model's training set, serving as effective tools for evaluating privacy leakage of vision models. However, existing MIAs implicitly assume honest query inputs, and their adversarial robustness remains unexplored. We show that MIAs for vision models expose a previously overlooked adversarial surface: adversarial membership manipulation, where imperceptible perturbations can reliably push non-member images into the member region of state-of-the-art MIAs. In this paper, we provide the first unified perspective on this phenomenon by analyzing its mechanism and implications. We begin by demonstrating that adversarial membership fabrication is consistently effective across diverse architectures and datasets. We then reveal a distinctive geometric signature--a characteristic gradient-norm collapse trajectory--that reliably separates fabricated from true members despite their nearly identical semantic representations. Building on this insight, we introduce a principled detection strategy grounded in gradient-geometry signals and develop a robust inference framework that substantially mitigates adversarial manipulation. Extensive experiments show that fabrication is broadly effective, while our detection and robust inference strategies significantly enhance resilience. This work establishes the first comprehensive framework for adversarial membership manipulation in vision models.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2b7d4203001d68082d7895500121b6e8CVF · CVPR 2026

Prime Once, then Reprogram Locally: An Efficient Alternative to Black-Box Service Model Adaptation

Zhang, Yunbei, Cai, Chengyi, Liu, Feng, Hamm, Jihun

原文摘要

Adapting closed-box service models (i.e., APIs) for target tasks typically relies on reprogramming via Zeroth-Order Optimization (ZOO). However, this standard strategy is known for extensive, costly API calls and often suffers from slow, unstable optimization. Furthermore, we observe that this paradigm faces new challenges with modern APIs (e.g., GPT-4o). These models can be less sensitive to the input perturbations ZOO relies on, thereby hindering performance gains. To address these limitations, we propose an Alternative efficient Reprogramming approach for Service models (AReS). Instead of direct, continuous closed-box optimization, AReS initiates a single-pass interaction with the service API to prime an amenable local pre-trained encoder. This priming stage trains only a lightweight layer on top of the local encoder, making it highly receptive to the subsequent glass-box (white-box) reprogramming stage performed directly on the local model. Consequently, all subsequent adaptation and inference rely solely on this local proxy, eliminating all further API costs. Experiments demonstrate AReS's effectiveness where prior ZOO-based methods struggle: on GPT-4o, AReS achieves a +27.8% gain over the zero-shot baseline, a task where ZOO-based methods provide little to no improvement. Broadly, across ten diverse datasets, AReS outperforms state-of-the-art methods (+2.5% for VLMs, +15.6% for standard VMs) while reducing API calls by over 99.99%. AReS thus provides a robust and practical solution for adapting modern closed-box models.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6cb5e2a5d3565548dda891ead1d79349CVF · CVPR 2026

Gamba: Mamba-based graph convolutional network with dynamic graph topology learning for action recognition

Zhou, Rouyi, Wu, Yangzhi, Wen, Jiajun, Gao, Can, Liu, Feng, Lai, Zhihui, Shen, Linlin

原文摘要

Existing graph models predominantly utilize self-attention mechanisms to model feature correlations between the joints of each sample, which not only neglects dynamic relation dependencies in temporal dimension but also leads to redundant computation and difficulty in establishing a unified framework for joint relation representation. To address these problems, this paper develops a Mamba-based graph convolution network (Gamba) with dynamic graph topology learning. In order to capture local motion patterns, a node classification module has been developed to categorize motion joints into distinct types. To the best of our knowledge, this is the first work to assign motion joints with label information to facilitate correlation learning. To capture the underlying relation of the joints of different categories, the state space model is introduced to process enhanced temporal features, aiming to learn dynamic adjacency matrices for long-range dependencies of the joints across different categories. The proposed framework not only facilitates an adaptive focus on spatio-temporal feature modeling but also has less computational complexity than traditional self-attention-based approaches. Extensive experiments on the public NTU RGB+D 60/120 and NW-UCLA benchmark datasets demonstrate the superiority of Gamba over state-of-the-art methods in recognition accuracy. The source code of Gamba is available at https://github.com/RCEricZhou/Gamba.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:824ccf5c72d919849aaac577862c0fafCVF · CVPR 2026

BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation

Saluja, Rachit, Cihangir, Asli, Deng, Ruining, Paetzold, Johannes C., Liu, Fengbei, Sabuncu, Mert R.

原文摘要

Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data augmentation schemes; and collecting more labeled data. We take a different view, arguing that part of the problem lies in how the background is modeled. Common lesion segmentation collapses all non-lesion pixels into a single "background" class, ignoring the rich anatomical context in which lesions appear. In reality, the background is highly heterogeneous--composed of tissues, organs, and other structures that can now be labeled manually or inferred automatically using existing segmentation models.In this paper, we argue that training with fine-grained labels that sub-divide the background class, which we call BackSplit, is a simple yet powerful paradigm that can offer a significant performance boost without increasing inference costs. From an information theoretic standpoint, we prove that BackSplit increases the expected Fisher Information relative to conventional binary training, leading to tighter asymptotic bounds and more stable optimization. With extensive experiments across multiple datasets and architectures, we empirically show that BackSplit consistently boosts small-lesion segmentation performance, even when auxiliary labels are generated automatically using pretrained segmentation models. Additionally, we demonstrate that auxiliary labels derived from interactive segmentation frameworks exhibit the same beneficial effect, demonstrating its robustness, simplicity, and broad applicability.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:a850eee4e9aa1faa1c37a5c6fcb4571aCVF · CVPR 2026

From 3D Pose to Prose: Biomechanics-Grounded Vision-Language Coaching

Ji, Yuyang, Shen, Yixuan, Zhu, Shengjie, Kong, Yu, Liu, Feng

原文摘要

We present BioCoach, a biomechanics-grounded vision-language framework for fitness coaching from streaming video. BioCoach fuses two signals, visual appearance and 3D skeletal kinematics, through a novel three-stage pipeline: an exercise-specific degree-of-freedom selector that focuses analysis on salient joints; a structured biomechanical context that pairs individualized morphometrics with cycle and constraint analysis; and a vision-biomechanics conditioned feedback module that applies cross-attention to generate precise, actionable text. Using parameter-efficient training that freezes the vision and language backbones, BioCoach yields transparent, personalized reasoning rather than pattern matching. To enable learning and fair evaluation, we augment QEVD-fit-coach with biomechanics-oriented feedback to create QEVD-bio-fit-coach, and we introduce a biomechanics-aware LLM judge metric. BioCoach delivers clear gains on QEVD-bio-fit-coach across lexical and judgment metrics while maintaining temporal triggering; on the original QEVD-fit-coach, it improves text quality and correctness with near-parity timing, demonstrating that explicit kinematics and constraints are key to accurate, phase-aware coaching.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:bdefca5a941b6501c3e997151afcd8bbCVF · CVPR 2026

UniCorrn: Unified Correspondence Transformer Across 2D and 3D

Goswami, Prajnan, Ding, Tianye, Liu, Feng, Jiang, Huaizu

原文摘要

Visual correspondence across image-to-image (2D-2D), image-to-point cloud (2D-3D), and point cloud-to-point cloud (3D-3D) geometric matching forms the foundation for numerous 3D vision tasks. Despite sharing a similar problem structure, current methods use task-specific designs with separate models for each modality combination. We present UniCorn, the first correspondence model with shared weights that unifies geometric matching across all three tasks. Our key insight is that Transformer attention naturally captures cross-modal feature similarity. We propose a dual-stream decoder that maintains separate appearance and positional feature streams. This design enables end-to-end learning through stack-able layers while supporting flexible query-based correspondence estimation across heterogeneous modalities. Our architecture employs modality-specific backbones followed by shared encoder and decoder components, trained jointly on diverse data combining pseudo point clouds from depth maps with real 3D correspondence annotations. UniCorn achieves competitive performance on 2D-2D matching and surpasses prior state-of-the-art by 8% on 7Scenes (2D-3D) and 10% on 3DLoMatch (3D-3D) in registration recall. Code is provided at \href https://github.com/neu-vi/UniCorrn github.com/neu-vi/UniCorrn .

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:c4592b79cc9292db52d19bc5e79849f8CVF · CVPR 2026

H^2A^2: Homogeneity-Aware and Heterogeneity-Aware Feature Perception for Unified Indoor 3D Object Detection

Xie, Tao, An, Tao, Liu, Feng, Jin, Wensheng, Li, Zhengyu, Zhao, Lijun, Li, Ruifeng

原文摘要

In this work, we observe that for indoor 3D object detection, fundamental geometric cues induce homogeneous spatial responses across scenes, whereas scene-specific structure yields heterogeneous signatures. However, existing detectors lack effective mechanisms to jointly extract and exploit such dual properties, which imposes inherent limitations on detection performance. Guided by this insight, we propose H^2A^2, a homogeneity-aware and heterogeneity-aware feature perception network for unified indoor 3D object detection under cross-scene training paradigms.Technically, we introduce a structural-feature-aware kernel selection (SF-KS) method, which encompasses three core components:(i) task-aware linear modulation, a channel-wise affine transformation that strengthens scene-structural feature representation; (ii) kernel weight selection strategy that integrates an offset validity prior to suppress non-informative cross-scene transfer while utilizing a structural consistency posterior to capture scene-homogeneous cues. and (iii) task-aware channel gating that suppresses scene-irrelevant feature responses. Overall, SF-KS enables the precise optimization of homogeneous features while specializing in scene-specific heterogeneous ones. In addition, to stabilize cross-scene optimization, we further introduce norm-based gradient homogenization (NGH) algorithm, which normalizes and dynamically reweights per-task gradient norms to mitigate conflicts and promote consistent updates. Extensive experiments on diverse indoor benchmarks show that H^2A^2 delivers consistent gains over strong baselines and improves cross-scene generalization.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e551fda5a55b2f237c4fe317c2421479CVF · CVPR 2026

From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding

Liu, Yuyuan, Ji, Yiping, Le, Anjie, Zhu, Jiayuan, Pan, Jiazhen, Peng, Can, Deng, Jiajun, Liu, Fengbei 等 9 位作者

原文摘要

Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing methods, mainly based on GRPO, assign rewards at the response level. Such sparse reward leads to minimal learning signals when all candidate responses are failed in challenging scenarios.In this work, we propose a group-revision optimisation paradigm that enhances learning on hard cases. It begins with a sampled initial response and generates a set of revised candidates to explore improved grounding outcomes. Inspired by reward shaping, we introduce a consolidation process that quantifies each candidate's improvement over the initial attempt and converts it into informative shaping signals.These signals are used to both refine the reward and modulate the advantage, amplifying the influence of high-quality revisions.Our method achieves consistent gains across referring and reasoning segmentation, REC, and counting benchmarks compared with prior GRPO-based models. Code is available at https://github.com/yyliu01/GroupRevision.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e79b13cca0f7089ff072f623e7e2515bCVF · CVPR 2026

RNED: Rotary Number Encoding and Decoding for Medical VLMs

Liu, Fengbei, Kwak, Sunwoo, Nizam, Nusrat, Richter, Ilan, Beecy, Ashley, Raikhelkar, Jayant, Estrin, Deborah, Sabuncu, Mert R.

原文摘要

Vision-Language Models (VLMs) are increasingly adopted for medical applications, but their clinical utility is limited by a core weakness in quantitative reasoning. This limitation affects tasks ranging from regression of lesion sizes to prediction of bounding-box coordinates and stems from the discrete tokenization schemes underlying Large Language Models (LLMs). To address this, we propose Rotary Number Encoding and Decoding (RNED), a principled method for embedding continuous numerical values directly in the representation space of a VLM. Analogous to rotary position encoding, RNED represents a scalar by applying a number-specific rotation matrix to a dedicated numeric token embedding. This norm-preserving transformation maintains ordinal structure over a wide numerical range and integrates seamlessly with pretrained model weights. For decoding, we introduce a robust score-matching-based scheme to recover continuous values from hidden states in the presence of stochastic noise. We evaluate RNED on two quantitative tasks: radiological measurement estimation and medical visual grounding. On both internal and public benchmarks, RNED consistently outperforms existing VLM baselines. Together, these results show that RNED offers a robust, generalizable solution for numerical reasoning in medical VLMs, enabling models that are both quantitatively reliable and clinically applicable.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e99b1404f5ec3103b6300e1a1089da10CVF · CVPR 2026

Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization

Hong, Mingbo, Liu, Feng, Gevaert, Caroline, Vosselman, George, Cheng, Hao

原文摘要

Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on confounders (e.g., illumination, co-occurrence, and style) from the source domain, leading to spurious correlations that hinder generalization. To this end, this paper proposes a novel Basis-driven framework for domain generalization, namely Bridge, that incorporates causal inference into object detection. By learning the low-rank bases for front-door adjustment, Bridge blocks confounders' effects to mitigate spurious correlations, while simultaneously refining representations by filtering redundant and task-irrelevant components.Bridge can be seamlessly integrated with both discriminative (e.g., DINOv2/3, SAM) and generative (e.g., Stable Diffusion) Vision Foundation Models (VFMs). Extensive experiments across multiple domain generalization object detection datasets, i.e., Cross-Camera, Adverse Weather, Real-to-Artistic, Diverse Weather Datasets, and Diverse Weather DroneVehicle (our newly augmented real-world UAV-based benchmark), underscore the superiority of our proposed method over previous state-of-the-art approaches. The project page is available at: https://mingbohong.github.io/Bridge/.

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 ↗