GenAI4ELab/papercli-papers huggingface dataset:
UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass
Authors: Mengfei Li; Peng Li; Zheng Zhang; Jiahao Lu; Chengfeng Zhao; Wei Xue; Qifeng Liu; Sida Peng; Wenxiao Zhang; Wenhan Luo; Yuan Liu; Yike Guo
Venue: CVPR 2026 (main)

Abstract: We present UniSH, a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction. A key challenge in this domain is the scarcity of large-scale, annotated real-world data, forcing a reliance on synthetic datasets. This reliance introduces a significant sim-to-real domain gap, leading to poor generalization, low-fidelity human geometry, and poor alignment on in-the-wild videos. To address this, we propose an innovative training paradigm that effectively leverages unlabeled in-the-wild data. Our framework bridges strong, disparate priors from scene reconstruction and HMR, and is trained with two core components: (1) a robust distillation strategy to refine human surface details by distilling high-frequency details from an expert depth model, and (2) a two-stage supervision scheme, which first learns coarse localization on synthetic data, then fine-tunes on real data by directly optimizing the geometric correspondence between the SMPL mesh and the human point cloud. This approach enables our feed-forward model to jointly recover high-fidelity scene geometry, human point clouds, camera parameters, and coherent, metric-scale SMPL bodies, all in a single forward pass. Extensive experiments demonstrate that our model achieves state-of-the-art performance on human-centric scene reconstruction and delivers highly competitive results on global human motion estimation, comparing favorably against both optimization-based frameworks and HMR-only methods. Project page: https://murphylmf.github.io/UniSH/.
V-Attack: Targeting Disentangled Value Features for Controllable Adversarial Attacks on LVLMs
Authors: Sen Nie; Jie Zhang; Jianxin Yan; Shiguang Shan; Xilin Chen
Venue: CVPR 2026 (main)

Abstract: Adversarial attacks have evolved from simply disrupting predictions on conventional task-specific models to the more complex goal of manipulating image semantics in Large Vision-Language Models (LVLMs). However, existing methods struggle with controllability and cannot precisely manipulate the semantics of specific concepts in an image. We attribute this limitation to semantic entanglement in the patch-token representations that adversarial attacks typically operate on: global context aggregated by self-attention in the vision encoder dominates patch features, making them unreliable for precise local semantic manipulation. Our systematic investigation reveals a key insight: value features, computed within the transformer attention block, provide much more precise handles for manipulation. We show that these value features suppress global-context channels, allowing them to retain high-entropy, disentangled local semantic information. Building on this discovery, we propose V-Attack, a novel method for precise local semantic attacks. V-Attack targets value features and introduces two core components: (1) a Self-Value Enhancement module to refine the intrinsic semantic richness of value features, and (2) a Text-Guided Value Manipulation module that uses text prompts to locate the source concept and optimize it toward a target concept. By bypassing entangled patch features, V-Attack achieves highly effective semantic control. Extensive experiments across diverse LVLMs, including LLaVA, InternVL, DeepSeek-VL, and GPT-4o, show that V-Attack improves attack success rate by an average of 36% over state-of-the-art methods, exposing critical vulnerabilities in modern vision-language understanding.
SpaceMind: Camera-Guided Modality Fusion for Spatial Reasoning in Vision-Language Models
Authors: Ruosen Zhao; Zhikang Zhang; Jialei Xu; Jiahao Chang; Dong Chen; Lingyun Li; Weijian Sun; Zizhuang Wei
Venue: CVPR 2026 (main)

Abstract: Large vision-language models (VLMs) show strong multimodal understanding but still struggle with 3D spatial reasoning, such as distance estimation, size comparison, and cross-view consistency. Existing 3D-aware methods either depend on auxiliary 3D information or enhance RGB-only VLMs with geometry encoders through shallow feature fusion. We propose SpaceMind, a multimodal large language model trained on SpaceMind-700K for spatial reasoning from RGB inputs. The model adopts a dual-encoder architecture integrating VGGT as a spatial understanding encoder and InternViT as a 2D visual encoder. The key idea is to treat the camera representation as an active guiding modality rather than passive metadata. Specifically, SpaceMind introduces a lightweight Camera-Guided Modality Fusion module before the language model to replace shallow fusion. It applies camera-conditioned biasing to spatial tokens, assigns query-independent weights reflecting their geometric importance, and uses the camera embedding to gate the fused representation. Empirically, SpaceMind establishes new state-of-the-art results on VSI-Bench, SQA3D and SPBench, surpassing both open and proprietary systems on VSI-Bench and SPBench by large margins and achieving state-of-the-art performance on SQA3D. These results demonstrate that camera-guided modality fusion is an effective and practical inductive bias for equipping VLMs with genuinely spatially grounded intelligence. We will release code and model checkpoints to support future research.
Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness
Authors: Yehonatan Elisha; Oren Barkan; Noam Koenigstein
Venue: CVPR 2026 (main)

Abstract: Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features. Existing regularization methods, typically relying on simple foreground-background masks, which fail to capture the fine-grained semantic concepts that define an object (e.g., "long beak" and "wings" for a "bird"). As a result, these methods provide limited robustness to distribution shifts. To address this limitation, we introduce a novel finetuning framework that steers model reasoning toward concept-level semantics. Our approach optimizes the model's internal relevance maps to align with spatially grounded concept masks. These masks are generated automatically, without manual annotation: class-relevant concepts are first proposed using an LLM-based, label-free method, and then segmented using a VLM. The finetuning objective aligns relevance with these concept regions while simultaneously suppressing focus on spurious background areas. Notably, this process requires only a minimal set of images and uses half of the dataset classes. Extensive experiments on five out-of-distribution benchmarks demonstrate that our method improves robustness across multiple ViT-based models. Furthermore, we show that the resulting relevance maps exhibit stronger alignment with semantic object parts, offering a scalable path toward more robust and interpretable vision models. Finally, we confirm that concept-guided masks provide more effective supervision for model robustness than conventional segmentation maps, supporting our central hypothesis.
3D Space as a Scratchpad for Editable Text-to-Image Generation
Authors: Oindrila Saha; Vojtech Krs; Radomir Mech; Subhransu Maji; Matheus Gadelha; Kevin Blackburn-Matzen
Venue: CVPR 2026 (main)

Abstract: Recent progress in large language models (LLMs) has shown that reasoning improves when intermediate thoughts are externalized into explicit workspaces, such as chain-of-thought traces or tool-augmented reasoning. Yet, visual language models (VLMs) lack an analogous mechanism for spatial reasoning, limiting their ability to generate images that accurately reflect geometric relations, object identities, and compositional intent. We introduce the concept of a spatial scratchpad -- a 3D reasoning substrate that bridges linguistic intent and image synthesis. Given a text prompt, our framework parses subjects and background elements, instantiates them as editable 3D meshes, and employs agentic scene planning for placement, orientation, and viewpoint selection. The resulting 3D arrangement is rendered back into the image domain with identity-preserving cues, enabling the VLM to generate spatially consistent and visually coherent outputs. Unlike prior 2D layout-based methods, our approach supports intuitive 3D edits that propagate reliably into final images. Empirically, it achieves a 32% improvement in text alignment on GenAI-Bench, demonstrating the benefit of explicit 3D reasoning for precise, controllable image generation. Our results highlight a new paradigm for vision-language models that deliberate not only in language, but also in space. Code and visualizations at https://oindrilasaha.github.io/3DScratchpad/
Confusion-Aware Spectral Regularizer for Long-Tailed Recognition
Authors: Ziquan Zhu; Gaojie Jin; Hanruo Zhu; Si-Yuan Lu; Yunxiao Zhang; Zeyu Fu; Ronghui Mu; Guoqiang Zhang; Zhao Sun; Yuhang Xia; Jiaxing Shang; Xiang Li; Lu Liu; Tianjin Huang
Venue: CVPR 2026 (main)

Abstract: Long-tailed image classification remains a long-standing challenge, as real-world data typically follow highly imbalanced distributions where a few head classes dominate and many tail classes contain only limited samples. This imbalance biases feature learning toward head categories and leads to significant degradation on rare classes. Although recent studies have proposed re-sampling, re-weighting, and decoupled learning strategies, the improvement on the most underrepresented classes still remains marginal compared with overall accuracy. In this work, we present a confusion-centric perspective for long-tailed recognition that explicitly focuses on worst-class generalization. We first establish a new theoretical framework of class-specific error analysis, which shows that the worst-class error can be tightly upper-bounded by the spectral norm of the frequency-weighted confusion matrix and a model-dependent complexity term. Guided by this insight, we propose the Confusion-Aware Spectral Regularizer (CAR) that minimizes the spectral norm of the confusion matrix during training to reduce inter-class confusion and enhance tail-class generalization. To enable stable and efficient optimization, CAR integrates a Differentiable Confusion Matrix Surrogate and an EMA-based Confusion Estimator to maintain smooth and low-variance estimates across mini-batches. Extensive experiments across multiple long-tailed benchmarks demonstrates that CAR substantially improves both worst-class accuracy and overall performance. When combined with ConCutMix augmentation, CAR consistently surpasses exisiting state-of-the-art long-tailed learning methods under both the training-from-scratch setting (by 2.37%   4.83%) and the fine-tuning-from-pretrained setting (by 2.42%   4.17%) across ImageNet-LT, CIFAR100-LT, and iNaturalist datasets.
SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models
Authors: Senyu Fei; Siyin Wang; Li Ji; Ao Li; Shiduo Zhang; Liming Liu; Jinlong Hou; Jingjing Gong; Xianzhong Zhao; Xipeng Qiu
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting performance. Reinforcement learning (RL) is a vital post-training strategy to overcome these limits, yet current VLA-RL methods, including group-based optimization approaches, are crippled by severe reward sparsity. Relying on binary success indicators wastes valuable information in failed trajectories, resulting in low training efficiency. To solve this, we propose Self-Referential Policy Optimization (SRPO), a novel VLA-RL framework. SRPO eliminates the need for external demonstrations or manual reward engineering by leveraging the model's own successful trajectories, generated within the current training batch, as a self-reference. This allows us to assign a progress-wise reward to failed attempts. A core innovation is the use of Latent World Representations to measure behavioral progress robustly. Instead of relying on raw pixels or requiring domain-specific fine-tuning, we utilize the compressed, transferable encodings from a world model's latent space. These representations naturally capture progress patterns across environments, enabling accurate, generalized trajectory comparison. Empirical evaluations on the LIBERO benchmark demonstrate SRPO's efficiency and effectiveness. Starting from a supervised baseline with 48.9% success, SRPO achieves a new state-of-the-art success rate of 99.2% in just 200 RL steps, representing a 103% relative improvement without any extra supervision. Furthermore, SRPO shows substantial robustness, achieving a 167% performance improvement on the LIBERO-Plus benchmark.
FlashVGGT: Efficient and Scalable Visual Geometry Transformers with Compressed Descriptor Attention
Authors: Zipeng Wang; Dan Xu
Venue: CVPR 2026 (main)

Abstract: 3D reconstruction from multi-view images is a core challenge in computer vision. Recently, feed-forward methods have emerged as efficient and robust alternatives to traditional per-scene optimization techniques. Among them, state-of-the-art models like the Visual Geometry Grounding Transformer (VGGT) leverage full self-attention over all image tokens to capture global relationships. However, this approach suffers from poor scalability due to the quadratic complexity of self-attention and the large number of tokens generated in long image sequences.In this work, we introduce FlashVGGT, an efficient alternative that addresses this bottleneck through a descriptor-based attention mechanism. Instead of applying dense global attention across all tokens, FlashVGGT compresses spatial information from each frame into a compact set of **descriptor tokens**. Global attention is then computed as cross-attention between the full set of image tokens and this smaller descriptor set, significantly reducing computational overhead.Moreover, the compactness of the descriptors enables online inference over long sequences via a chunk-recursive mechanism that reuses cached descriptors from previous chunks. Experimental results show that FlashVGGT achieves reconstruction accuracy competitive with VGGT while reducing inference time to just **9.3%** for 1,000 images, and scaling efficiently to sequences exceeding **3,000** images.
Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification
Authors: Qihao Liu; Chengzhi Mao; Yaojie Liu; Alan Yuille; Wen-Sheng Chu
Venue: CVPR 2026 (main)

Abstract: Conventional evaluation methods for multimodal LLMs (MLLMs) lack interpretability and are often insufficient to fully disclose significant capability gaps across models. To address this, we introduce AuditDM, an automated framework that actively discovers and rectifies MLLM failure modes by auditing their divergence. AuditDM fine-tunes an MLLM as an auditor via reinforcement learning to generate challenging questions and counterfactual images that maximize disagreement among target models. Once trained, the auditor uncovers diverse, interpretable exemplars that reveal model weaknesses and serve as annotation-free data for rectification. When applied to SoTA models like Gemma-3 and PaliGemma-2, AuditDM discovers more than 20 distinct failure types. Fine-tuning on these discoveries consistently improves all models across 16 benchmarks, and enables a 3B model to surpass its 28B counterpart. Our results suggest that as data scaling hits diminishing returns, targeted model auditing offers an effective path to model diagnosis and improvement.
Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning
Authors: Linge Wang; Yingying Chen; Bingke Zhu; Lu Zhou; Jinqiao Wang
Venue: CVPR 2026 (main)

Abstract: Recent advances in audio-visual representation learning have shown the value of combining contrastive alignment with masked reconstruction.  However, jointly optimizing these objectives in a single forward pass forces the contrastive branch to rely on randomly visible patches designed for reconstruction rather than cross-modal alignment, introducing semantic noise and optimization interference. We propose TG-DP, a Teacher-Guided Dual-Path framework that decouples reconstruction and alignment into separate optimization paths. By disentangling the masking regimes of the two branches, TG-DP enables the contrastive pathway to use a visibility pattern better suited to cross-modal alignment. A teacher model further provides auxiliary guidance for organizing visible tokens in this branch, helping reduce interference and stabilize cross-modal representation learning. TG-DP achieves state-of-the-art performance in zero-shot retrieval. On AudioSet, it improves R@1 from 35.2% to 37.4% for video-to-audio retrieval and from 27.9% to 37.1% for audio-to-video retrieval. The learned representations also remain semantically robust, achieving state-of-the-art linear-probe performance on AS20K and VGGSound. Taken together, our results suggest that decoupling multimodal objectives and introducing teacher-guided structure into the contrastive pathway provide an effective framework for improving large-scale audio-visual pretraining.
The Geometry of Robustness: Optimizing Loss Landscape Curvature and Feature Manifold Alignment for Robust Finetuning of Vision-Language Models
Authors: Shivang Chopra; Shaunak Halbe; Chengyue Huang; Brisa Maneechotesuwan; Zsolt Kira
Venue: CVPR 2026 (main)

Abstract: Fine-tuning approaches for Vision-Language Models (VLMs) face a critical three-way trade-off between In-Distribution (ID) accuracy, Out-of-Distribution (OOD) generalization, and adversarial robustness. Existing robust fine-tuning strategies resolve at most two axes of this trade-off. Generalization-preserving methods retain ID/OOD performance but leave models vulnerable to adversarial attacks, while adversarial training improves robustness to targeted attacks but degrades ID/OOD accuracy. Our key insight is that the robustness trade-off stems from two geometric failures: sharp, anisotropic minima in parameter space and unstable feature representations that deform under perturbation. To address this, we propose GRACE (Gram-aligned Robustness via Adaptive Curvature Estimation), a unified fine-tuning framework that jointly regularizes the parameter-space curvature and feature-space invariance for VLMs. Grounded in Robust PAC-Bayes theory, GRACE employs adaptive weight perturbations scaled by local curvature to promote flatter minima, combined with a feature alignment loss that maintains representation consistency across clean, adversarial, and OOD inputs. On ImageNet fine-tuning of CLIP models, GRACE simultaneously improves ID accuracy by 10.8%, and adversarial accuracy by 13.5% while maintaining 57.0% OOD accuracy (vs. 57.4% zero-shot baseline). Geometric analysis confirms that GRACE converges to flatter minima without feature distortion across distribution shifts, providing a principled step toward generalized robustness in foundation VLMs.
PolarGuide-GSDR: 3D Gaussian Splatting Driven by Polarization Priors and Deferred Reflection for Real-World Reflective Scenes
Authors: Derui Shan; Qian Qiao; Hao Lu; Tao Du; Peng Lu
Venue: CVPR 2026 (main)

Abstract: Polarization-aware Neural Radiance Fields (NeRF) enables novel view synthesis of specular scenes but suffers from slow training, inefficient rendering, and material/viewpoint assumptions. 3D Gaussian Splatting (3DGS) supports real-time rendering but struggles with reflection reconstruction due to reflection-geometry entanglement. We propose PolarGuide-GSDR, a polarization-guided framework that leverages polarization information to separate specular reflections for guided reconstruction, and builds bidirectional iterative optimization between polarization and 3DGS: it first uses 3DGS geometric priors to disambiguate polarization, then uses refined polarization normal to guide 3DGS normals and Spherical Harmonics representations, achieving high-fidelity reflection separation and full-scene reconstruction without restrictive material assumptions. Experiments on public and self-collected datasets show that PolarGuide-GSDR achieves state-of-the-art performance in specular reconstruction, normal estimation, and novel view synthesis, while maintaining real-time rendering. To our knowledge, this is the first framework embedding polarization priors into 3DGS optimization, offering superior interpretability and real-time performance for complex reflective scenes.
Parallelised Differentiable Straightest Geodesics for 3D Meshes
Authors: Hippolyte Verninas; Caner Korkmaz; Stefanos Zafeiriou; Tolga Birdal; Simone Foti
Venue: CVPR 2026 (main)

Abstract: Machine learning has been progressively generalised to operate within non-Euclidean domains, but geometrically accurate methods for learning on surfaces are still falling behind. The lack of closed-form Riemannian operators, the non-differentiability of their discrete counterparts, and poor parallelisation capabilities have been the main obstacles to the development of the field on meshes. A principled framework to compute the exponential map on Riemannian surfaces discretised as meshes is straightest geodesics, which also allows to trace geodesics and parallel-transport vectors as a by-product. We provide a parallel GPU implementation and derive two different methods for differentiating through the straightest geodesics, one leveraging an extrinsic proxy function and one based upon a geodesic finite differences scheme. After proving our parallelisation performance and accuracy, we demonstrate how our differentiable exponential map can supercharge geometrically-correct learning and optimisation pipelines. In particular, to showcase the versatility of our method, we propose a new geodesic convolutional layer, a new flow matching method for learning on meshes, and a second-order optimiser that we apply to centroidal Voroni tesselation. Our code, pre-trained models, and pip-installable library will be made available upon publication.
PhysInOne: Visual Physics Learning and Reasoning in One Suite
Authors: Siyuan Zhou; Hejun Wang; Hu Cheng; Jinxi Li; Dongsheng Wang; Junwei Jiang; Yixiao Jin; Jiayue Huang; Shiwei Mao; Shangjia Liu; Yafei Yang; Hongkang Song; Shenxing Wei; Zihui Zhang; Bing Wang; Zhihua Wang; Chuhang Zou; Bo Yang
Venue: CVPR 2026 (main)

Abstract: We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike existing datasets limited to merely hundreds or thousands of examples, PhysInOne provides 2 million videos across 153,810 dynamic 3D scenes, covering 71 basic physical phenomena in mechanics, optics, fluid dynamics, and magnetism. Distinct from previous works, our scenes feature multiobject interactions against complex backgrounds, with comprehensive ground-truth annotations including 3D geometry, semantics, dynamic motion, physical properties, and text descriptions. We demonstrate PhysInOne's efficacy across four emerging applications: physics-aware video generation, long-/short-term future frame prediction, physical property estimation, and motion transfer. Experiments show that fine-tuning foundation models on PhysInOne significantly enhances physical plausibility, while also exposing critical gaps in modeling complex physical dynamics and estimating intrinsic properties. As the largest dataset of its kind, orders of magnitude beyond prior works, PhysInOne establishes a new benchmark for advancing physics-grounded world models in generation, simulation, and embodied AI.