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Weiyu Zhao, Ru Li, Jiaqi Liu, Sizhe Zhao, Qinglin Liu, Shengping Zhang

Open-vocabulary 3D object affordance grounding aims to identify functional regions of objects given arbitrary semantic descriptions. However, existing methods often rely on fixed training categories and geometric priors, lacking geometric invariance and analogical reasoning capabilities. Since there exists a significant domain gap when transferring affordance knowledge learned from 2D images to 3D point clouds, existing methods struggle to generalize well to objects with diverse shapes or unseen categories, and fail to perform effective category reasoning.To address these challenges, we propose **QueryMe**, a **Query**-driven framework that learns from **M**ultimodal **e**vidence spaces to achieve open-vocabulary 3D affordance grounding.The proposed approach is to project human-object interaction images into 3D space, employ an Adaptive Spatial Attention module to focus on key interaction regions, and introduce a multimodal query structure to retrieve available geometrically consistent functional parts within the point cloud, effectively fusing visual, linguistic, and geometric cues.Leveraging attention-based query mechanisms, our method adaptively localizes affordance regions and performs analogy reasoning through geometric similarity, thereby exhibiting strong generalization to unseen scenes and objects. Experimental results demonstrate that QueryMe consistently outperforms state-of-the-art approaches, with the AUC improving by 4.19% compared to previous work for unseen affordance grounding tasks.

Jaeyun Jang, Seunghui Shin, Taeho Park, Hyoseok Hwang

Perspective-aware spatial reasoning involves understanding spatial relationships from specific viewpoints--either egocentric (observer-centered) or allocentric (object-centered).While vision-language models (VLMs) perform well in egocentric settings, their performance deteriorates when reasoning from allocentric viewpoints, where spatial relations must be inferred from the perspective of objects within the scene.In this study, we address this underexplored challenge by introducing Symbolic Projective Layout (SymPL), a framework that reformulates allocentric reasoning into symbolic-layout forms that VLMs inherently handle well.By leveraging four key factors--projection, abstraction, bipartition, and localization--SymPL converts allocentric questions into structured symbolic-layout representations.Extensive experiments demonstrate that this reformulation substantially improves performance in both allocentric and egocentric tasks, enhances robustness under visual illusions and multi-view scenarios, and that each component contributes critically to these gains.These results show that SymPL provides an effective and principled approach for addressing complex perspective-aware spatial reasoning.

Jonas Ernst, Wolfgang Boettcher, Lukas Hoyer, Jan Eric Lenssen, Bernt Schiele

We present Rewis3d, a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images. Obtaining dense, pixel-level annotations remains a costly bottleneck for training segmentation models. Alleviating this issue, sparse annotations offer an efficient weakly-supervised alternative. However, they still incur a performance gap. To address this, we introduce a novel approach that leverages 3D scene reconstruction as an auxiliary supervisory signal. Our key insight is that 3D geometric structure recovered from 2D videos provides strong cues that can propagate sparse annotations across entire scenes. Specifically, a dual student-teacher architecture enforces semantic consistency between 2D images and reconstructed 3D point clouds, using state-of-the-art feed-forward reconstruction to generate reliable geometric supervision. Extensive experiments demonstrate that Rewis3d achieves state-of-the-art performance in sparse supervision, outperforming existing approaches by 2-7% without requiring additional labels or inference overhead. Project page: https://rewis3d-mpi.github.io/Rewis3d/

Hebaixu Wang, Jing Zhang, Haoyang Chen, Haonan Guo, Di Wang, Jiayi Ma, Bo Du

Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restoration. Most existing methods merely treat them as simple variants of stochastic interpolants, lacking a unified analytical perspective. Besides, they indiscriminately reconstruct images through global noise injection and removal, inevitably distorting undegraded regions due to imperfect reconstruction. To address these challenges, we propose the R esidual D iffusion B ridge M odel (RDBM). Specifically, we theoretically reformulate the stochastic differential equations of generalized diffusion bridge and derive the analytical formulas of its forward and reverse processes. Crucially, we leverage the residuals from given distributions to modulate the noise injection and removal, enabling adaptive restoration of degraded regions while preserving intact others. Additionally, we unravel the fundamental mathematical essence of existing bridge models, all of which are special cases of RDBM and empirically demonstrate the optimality of our proposed models. Extensive experiments are conducted to demonstrate the state-of-the-art performance of our method both qualitatively and quantitatively across diverse image restoration tasks. Code is publicly available at https://github.com/MiliLab/RDBM.

Fengyuan Zuo, Haiyan Jin, Yuanlin Zhang, Zhaolin Xiao, Bin Wang, Yuerong Mu

Dark optical flow estimation (DOFE) faces critical challenges: discriminative models are less robust to noise and struggle with weakened motion patterns, while diffusion models suffer from discontinuous flow fields and low efficiency. Flow matching (FM), though efficient, remains underexplored for conditional generation in DOFE. In this paper, we propose FlowFM, the first flow matching model tailored to DOFE tasks. Instead of conventional vector field regression, FlowFM proposes estimating the global transformation path constrained by the ground truth optical flow. It generates noisy flow by mixing Gaussian noise with ground truth, then performs a one-step denoising process conditioned on the initial flow field, cost volume, and contextual features for optimal accuracy and efficiency. FlowFM incorporates an implicit Fourier denoising decoder (IFDD) for reliable motion understanding. By leveraging the Fourier transform, IFDD uses amplitude to characterize motion intensity and phase to encode target spatial relationships within flow fields, then directly enhances amplitude to restore dark-caused motion information loss. Experiments show that FlowFM significantly outperforms state-of-the-art methods on the FCDN and VBOF benchmarks, setting a new performance record for DOFE.

Yue Li, Qi Ma, Runyi Yang, Mengjiao Ma, Bin Ren, Nikola Popovic, Nicu Sebe, Theo Gevers, Luc Van Gool, Danda Pani Paudel 等

While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D foundation models. Chorus employs a shared 3D encoder and teacher-specific projectors to learn from language-aligned, generalist, and object-aware teachers, encouraging a shared embedding space that captures signals from high-level semantics to fine-grained structure. We evaluate Chorus on a wide range of tasks: open-vocabulary semantic and instance segmentation, linear and decoder probing, data-efficient supervision, as well as LLM-based Q&A. Besides 3DGS, we also test Chorus on several benchmarks that only support point clouds by pretraining a variant using only Gaussians' centers, colors, estimated normals. Interestingly, this encoder shows strong transfer and outperforms the point clouds baseline while using 39.9xfewer training scenes. Finally, we propose a render-and-distill adaptation that facilitates out-of-domain finetuning. Our code and model is released at this codebase.

Xu Jiang, Bin Chen, Gehui Li, Yule Duan, Ronggang Wang, Jian Zhang

The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns from single-model scaling. Agentic T2I methods emerged to alleviate this bottleneck by using multiple models. However, existing agentic T2I methods suffer from three key challenges: reliance on expensive handcrafted priors or human annotations, rigid single-path decision mechanisms, and a neglect of inference efficiency. To address these challenges, we introduce OctoT2I, a novel agentic framework that reformulates the T2I task as a joint optimization of generation quality and inference efficiency. OctoT2I implements a stateful, multi-round routing strategy that adaptively selects the most suitable tool based on its knowledge and memory. This strategy is enabled by a knowledge base built from scratch by our novel Self-Evolving Mechanism. This mechanism, which requires no human supervision, first autonomously defines foundational Conceptual Dimensions (e.g., style, color, count) and then intelligently explores their combinations via an iterative "Propose--Solve--Evaluate--Learn" (PSEL) loop. The PSEL loop efficiently discovers each tool's capability frontier, driving continuous improvement without external guidance. Extensive experiments demonstrate that OctoT2I achieves competitive performance (0.96) on GenEval while delivering a 90.3% inference speedup and a 56.6% energy-efficiency gain over the leading baseline (Flow-GRPO), striking an exceptional balance between performance and efficiency. Code and models will be made available.

Siwei Han, Haonian Ji, Siyang Xin, Juanquan Shi, Shi Qiu, Xinyu Ye, Peng Xia, Jiaqi Liu, Zhaorun Chen, Yiyang Zhou 等

Automatically generating clear and accurate figures for research papers remains challenging, as it requires semantic understanding, precise structure, and visual aesthetics. Existing approaches struggle to balance fidelity and quality: large language model (LLM) code-based methods (e.g., SVG, Mermaid) are structured but inflexible, while image-generation models (e.g., GPT-Image-1, Nano Banana) produce hard-to-edit and often inaccurate figures. We present Paper2Figure, a dual multi-agent system with an interactive web platform for paper-to-figure generation. Generation Agents convert text into our designed FigScript language, encoding figure semantics, styles and layout. The web system renders the FigScript into an initial image, which Refinement Agents iteratively analyze to locate issues and revise the FigScript for improved logic, alignment, aesthetics and text accuracy. Crucially, users can further refine results through an intuitive web interface, ensuring full control over the final output. To evaluate Paper2Figure, we introduce Paper2Figure Bench, a benchmark comprising 100 academic figures with paired descriptions. Experiments demonstrate that Paper2Figure markedly improves accuracy by 12%, beauty by 13.5%, and completeness by 17.0% over state-of-the-art baselines in fully automatic generation without human adjustment. By combining automated generation with interactive edit, Paper2Figure bridges the gap between AI assistance and researcher control, offering a practical solution for high-quality academic figure creation.

Tanqiu Jiang, Jiacheng Liang, Rongyi Zhu, Jiawei Zhou, Fenglong Ma, Ting Wang

Large vision-language models (VLMs) are highly vulnerable to multimodal jailbreak attacks that exploit visual-textual interactions to bypass safety guardrails. In this paper, we present DTR, a novel inference-time defense that mitigates multimodal jailbreak attacks through optimizing the model's key-value (KV) caches. Rather than relying on curated safety-specific data or costly image-to-text conversion, we introduce a new formulation of the safety-relevant distributional shift induced by the visual modality. This formulation enables DTR to dynamically adjust visual token weights, minimizing the impact of adversarial visual inputs while preserving the model's general capabilities and inference efficiency. Extensive evaluation across diverse VLMs and attack benchmarks demonstrates that DTR outperforms existing defenses in both attack robustness and benign-task performance, marking the first successful application of KV cache optimization for safety enhancement in multimodal foundation models. The code for replicating DTR is available at: https://github.com/TanqiuJiang/DTR.

Wei Liu, Li Yang, Yufei Wang, Han Xiao, Boyu Cai, Weiming Hu

Spiking Neural Networks (SNNs) naturally process visual inputs across multiple timesteps, offering rich temporal dynamics and energy-efficient computation. However, the temporally invariant supervision commonly used in training tends to reinforce the same dominant response patterns across timesteps, leading to redundant representations and limiting temporal discriminability. To overcome this limitation, we introduce Temporal Representation Enhancement(TRE), a novel learning-to-forget paradigm that encourages more diverse and complementary temporal representations. TRE identifies high-contribution semantic patterns through class-specific contribution estimation and temporal accumulation, and selectively suppresses them using a dynamic modulation strategy. By redirecting the model's attention toward alternative yet informative semantic cues, TRE promotes the learning of complementary features across timesteps. This approach not only strengthens the temporal discriminative capacity of SNNs but also enables more effective multi-timestep learning by leveraging richer semantic information. Extensive experiments on both static image datasets and dynamic neuromorphic datasets demonstrate that TRE consistently improves classification accuracy and feature diversity across different SNN backbones.

Scott Xu, Dian Chen, Kelvin Wong, Chris Zhang, Kion Fallah, Raquel Urtasun

Accurately modeling agent behaviors is an important task in self-driving. It is also a task with many symmetries, such as equivariance to the order of agents and objects in the scene or equivariance to arbitrary roto-translations of the entire scene as a whole; i.e., SE(2)-equivariance. The transformer architecture is a ubiquitous tool for modeling these symmetries. While standard self-attention is inherently permutation equivariant, explicit pairwise relative positional encodings have been the standard for introducing SE(2)-equivariance. However, this approach introduces an additional cost that is quadratic in the number of agents, limiting its scalability to larger scenes and batch sizes. In this work, we propose DriveGATr, a novel transformer-based architecture for agent modeling that achieves SE(2)-equivariance without the computational cost of existing methods. Inspired by recent advances in geometric deep learning, DriveGATr encodes scene elements as multivectors in the 2D projective geometric algebra \mathbb R ^*_ 2,0,1 and processes them with a stack of equivariant transformer blocks. Crucially, DriveGATr models geometric relationships using standard attention between multivectors, eliminating the need for costly explicit pairwise relative positional encodings. Experiments on the Waymo Open Motion Dataset demonstrate that DriveGATr is comparable to the state-of-the-art in traffic simulation and establishes a superior Pareto front for performance vs computational cost.

Divyanshu Daiya, Aniket Bera

We present Sketch2Colab, which turns storyboard-style 2D sketches into coherent, object-aware 3D multi-human motion with fine-grained control over agents, joints, timing, and contacts. Diffusion-based motion generators offer strong realism but often rely on costly guidance for multi-entity control and degrade under strong conditioning. Sketch2Colab instead learns a sketch-conditioned diffusion prior and distills it into a rectified-flow student in latent space for fast, stable sampling. To make motion follow storyboards closely, we guide the student with differentiable objectives that enforce keyframes, paths, contacts, and physical consistency. Collaborative motion naturally involves discrete changes in interaction, such as converging, forming contact, cooperative transport, or disengaging, and a continuous flow alone struggles to sequence these shifts cleanly. We address this with a lightweight continuous-time Markov chain (CTMC) planner that tracks the active interaction regime and modulates the flow to produce clearer, synchronized coordination in human-object-human motion. Experiments on CORE4D and InterHuman show that Sketch2Colab outperforms baselines in constraint adherence and perceptual quality while sampling substantially faster than diffusion-only alternatives.

Jiwon Kang, Yeji Choi, JoungBin Lee, Wooseok Jang, Jinhyeok Choi, Taekeun Kang, Yongjae Park, Myungin Kim, Seungryong Kim

Face swapping aims to transfer the identity of a source face onto a target face while preserving target-specific attributes such as pose, expression, lighting, skin tone, and makeup. However, since real ground truth for face swapping is unavailable, achieving both accurate identity transfer and high-quality attribute preservation remains challenging. Recent diffusion-based approaches attempt to improve visual fidelity through conditional inpainting on masked target images, but the masked condition removes crucial appearance cues, resulting in plausible yet misaligned attributes. To address this limitation, we propose APPLE (Attribute-Preserving Pseudo-Labeling), a fully diffusion-based teacher-student framework for attribute-preserving face swapping. Our approach introduces a teacher design to produce pseudo-labels aligned with the target attributes through (1) a conditional deblurring formulation that improves the preservation of global attributes such as skin tone and illumination, and (2) an attribute-aware inversion scheme that further enhances fine-grained attribute preservation such as makeup. APPLE conditions the student on clean pseudo-labels rather than degraded masked inputs, enabling more faithful attribute preservation. As a result, APPLE achieves state-of-the-art performance in attribute preservation while maintaining competitive identity transferability.

Davide Allegro, Shiyao Li, Stefano Ghidoni, Vincent Lepetit

Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To address this limitation, we introduce the novel task of active mapping of moving objects, in which a mapping agent must plan its trajectory while compensating for the object's motion. Our approach, Paparazzo, provides a learning-free solution that robustly predicts the target's trajectory and identifies the most informative viewpoints from which to observe it, to plan its own path. We also contribute a comprehensive benchmark designed for this new task. Through extensive experiments, we show that Paparazzo significantly improves 3D reconstruction completeness and accuracy compared to several strong baselines, marking an important step toward dynamic scene understanding. Project page: https://davidea97.github.io/paparazzo-page/

Yujie Zhou, Pengyang Ling, Jiazi Bu, Yibin Wang, Yuhang Zang, Jiaqi Wang, Li Niu, Guangtao Zhai

The incorporation of online reinforcement learning (RL) into diffusion and flow-based generative models has recently gained attention as a powerful paradigm for aligning model behavior with human preferences. By leveraging stochastic sampling via Stochastic Differential Equations (SDEs) during the denoising phase, these models can explore a variety of denoising trajectories, enhancing the exploratory capacity of RL. However, despite their ability to discover potentially high-reward samples, current approaches often struggle to effectively align with preferences due to the sparsity and narrowness of reward feedback. To overcome this limitation, we introduce a novel framework called Granular-GRPO (G^2RPO), which enables fine-grained and comprehensive evaluation of sampling directions in the RL training of flow models. Specifically, we propose a Singular Stochastic Sampling mechanism that supports step-wise stochastic exploration while ensuring strong correlation between injected noise and reward signals, enabling more accurate credit assignment to each SDE perturbation. Additionally, to mitigate the bias introduced by fixed-granularity denoising, we design a Multi-Granularity Advantage Integration module that aggregates advantages computed across multiple diffusion scales, resulting in a more robust and holistic assessment of sampling trajectories. Extensive experiments on various reward models, including both in-domain and out-of-domain settings, demonstrate that our G^2RPO outperforms existing flow-based GRPO baselines, highlighting its effectiveness and generalization capability.

Kexin Shi, Hanwen Liu, Zeyang Song, Yang Liu, Jieyuan Zhang, Shuai Wang, Jibin Wu, Malu Zhang, Yang Yang

Spiking Neural Networks (SNNs) have gained significant attention due to their event-driven computational paradigm, making them promising for neuromorphic computing. In recent years, the integration of SNNs and Transformer architectures has made remarkable progress in various tasks. However, existing spiking self-attention mechanisms predominantly focus on spatial information while neglecting explicit temporal modeling, leading to suboptimal performance. In this paper, we introduce the Temporal Interaction Coefficient (TIC) to analyze temporal dependency patterns in these spatial-only attention mechanisms, revealing their limited temporal interactions and restricted pattern diversity. To overcome this issue, we propose the Multi-Delay Mixer (MD-Mixer), drawing inspiration from time delay mechanisms in the nervous system. Specifically, MD-Mixer introduces multiple temporal delays to perform effective time mixing and facilitate temporally enriched spatial attention. In addition, it can be integrated seamlessly into existing Spiking Transformers as a drop-in replacement while maintaining computational efficiency. Extensive evaluations on static and neuromorphic benchmarks demonstrate that MD-Mixer substantially improves the performance of Spiking Transformers, outperforming existing state-of-the-art (SOTA) methods. This work establishes MD-Mixer as an effective and general solution for temporal modeling in event-driven architectures.

Chen Feng, Zhuo Zhi, Zhao Huang, Jiawei Ge, Ling Xiao, Nicu Sebe, Georgios Tzimiropoulos, Ioannis Patras

Statistically consistent methods based on the noise transition matrix (T) offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of convergence to the optimal clean-data classifier. In practice, however, these methods are often outperformed by empirical approaches such as sample selection, and this gap is usually attributed to the difficulty of accurately estimating T. The common assumption is that, given a perfect T, noise-correction methods would recover their theoretical advantage. In this work, we put this longstanding hypothesis to a decisive test. We conduct experiments under idealized conditions, providing correction methods with a perfect, oracle transition matrix. Even under these ideal conditions, we observe that these methods still suffer from performance collapse during training. This compellingly demonstrates that the failure is not fundamentally a T-estimation problem, but stems from a more deeply rooted flaw. To explain this behaviour, we provide a unified analysis that links three levels: macroscopic convergence states, microscopic optimisation dynamics, and information-theoretic limits on what can be learned from noisy labels. Together, these results give a formal account of why ideal noise correction fails and offer concrete guidance for designing more reliable methods for learning with noisy labels.

Haoran Wang, Guoxi Huang, Fan Zhang, David Bull, Nantheera Anantrasirichai

Recent significant advances in 3D scene representation have been driven by 3D Gaussian Splatting (3DGS), which has enabled real-time rendering with photorealistic quality. 3DGS often requires a large number of primitives to achieve high fidelity, leading to redundant representations and high resource consumption, thereby limiting its scalability for complex or large-scale scenes. Consequently, effective pruning strategies and more expressive primitives that can reduce redundancy while preserving visual quality are crucial for practical deployment. We propose an efficient, integrated reconstruction-aware pruning strategy that adaptively determines pruning timing and refining intervals based on reconstruction quality, thus reducing model size while enhancing rendering quality. Moreover, we introduce a 3D Difference-of-Gaussians primitive that jointly models both positive and negative densities in a single primitive, improving the expressiveness of Gaussians under compact configurations. Our method significantly improves model compactness, achieving up to 90% reduction in Gaussian-count while delivering visual quality that is similar to, or in some cases better than, that produced by state-of-the-art methods.

Xucong Wang, Pengkun Wang, Zhe Zhao, Liheng Yu, Shuang Wang, Yang Wang

Multi-Label Recognition (MLR) based on Vision-Language Models (VLMs) aims to leverage their pre-trained knowledge to better adapt complex recognition scenarios, thereby enhancing model robustness. However, for realistic decentralized applications requiring federated learning, adapting VLMs to each client that possesses private and heterogeneous data can cause the model to overfit spurious label correlations, consequently triggering irrelevant categories when encountering new samples. To tackle this problem, we reconsider the federated learning for MLR with a causal model, in which we adopt a front-door adjustment and decouple the MLR modeling process by intermediate variables that magnify the oracle label co-occurrence. Guided by our analysis, we propose our FedMPT, the first method specifically designed for federated MLR. The core idea of FedMPT is to leverage generalizable conditions to steer federated MLR to mitigate erroneous label activations. To achieve this, FedMPT introduces an Large Language Model (LLM)-driven pipeline to decipher the underlying conditions that govern label dependencies. Furthermore, we introduce an optimal transport between the condition-enriched prompts and the image patches to uncover multiple region-level semantics. Finally, we generate synergistic predictions from different conditions with a crafted gating mechanism. Experiments on multiple benchmark datasets show that our proposed approach achieves competitive results and outperforms SOTA methods under varied settings.

Fahad Shamshad, Nils Lukas, Karthik Nandakumar

Invisible watermarking has become a critical mechanism for authenticating AI-generated image content, with major platforms deploying watermarking schemes at scale. However, evaluating the vulnerability of these schemes against sophisticated removal attacks remains essential to assess their reliability and guide robust design. In this work, we expose a fundamental vulnerability in invisible watermarks by reformulating watermark removal as a view synthesis problem. Our key insight is that generating a perceptually consistent alternative view of the same semantic content, akin to re-observing a scene from a shifted perspective, naturally removes the embedded watermark while preserving visual fidelity. This reveals a critical gap: watermarks robust to pixel-space and frequency-domain attacks remain vulnerable to semantic-preserving viewpoint transformations. We introduce a zero-shot diffusion-based framework that applies controlled geometric transformations in latent space, augmented with view-guided correspondence attention to maintain structural consistency during reconstruction. Operating on frozen pre-trained models without detector access or watermark knowledge, our method achieves state-of-the-art watermark suppression across 15 watermarking methods--outperforming 14 baseline attacks while maintaining superior perceptual quality across multiple datasets.