Text-to-image (T2I) diffusion models have made significant strides in generating high-quality images. However, progressively manipulating certain attributes of generated images to meet the desired user expectations remains challenging, particularly for content with rich details, such as human faces. Some studies have attempted to address this by training slider modules. However, they follow a One-for-One manner, where an independent slider is trained for each attribute, requiring additional training whenever a new attribute is introduced. This not only results in parameter redundancy accumulated by sliders but also restricts the flexibility of practical applications and the scalability of attribute manipulation. To address this issue, we introduce the All-in-One Slider, a lightweight module that decomposes the text embedding space into sparse, semantically meaningful attribute directions. Once trained, it functions as a general-purpose slider, enabling interpretable and fine-grained continuous control over various attributes. Moreover, by recombining the learned directions, the All-in-One Slider supports zero-shot manipulation of unseen attributes (e.g., races and celebrities) and the composition of multiple attributes. Extensive experiments demonstrate that our method enables accurate and scalable attribute manipulation, achieving notable improvements compared to previous methods. Furthermore, our method can be extended to integrate with the inversion framework to perform attribute manipulation on real images, broadening its applicability to various real-world scenarios. The code and trained model will be released.
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Quota-Calibrated Fine-Grained Alignment with Context-Aware Marginals for Text-based Person Retrieval
PDF ↗The core challenge in Text-based Person Retrieval (TPR) lies in establishing fine-grained, many-to-many semantic alignment between textual words and visual regions. Existing methods predominantly rely on pointwise similarity or attention mechanisms, implicitly assuming matches are independent and balanced. Consequently, under conditions of attribute overlap and substantial background noise, these methods often misallocate matching weights to non-discriminative regions or words, resulting in ambiguous matching outcomes. To address this, we propose QC-Align, a quota-calibrated fine-grained alignment framework guided by context-aware marginals. Specifically, we propose a Context-Aware Marginal Estimator (CAME) that dynamically assigns "matching quotas" to each word and visual region, and subsequently employs a Quota-Calibrated Transport (QCT) objective to explicitly constrain the matching quality each word and region can carry, thereby jointly optimizing the many-to-many correspondence between text and vision under these constraints. Notably, QC-Align is a parameter-free, plug-and-play training regularizer that requires no fine-grained annotations and incurs no inference overhead. Experiments on multiple mainstream person retrieval benchmarks demonstrate that QC-Align consistently improves baseline model performance, with greater gains and better interpretability in few-shot and cross-domain scenarios.
While modern generative models such as diffusion-based architectures have enabled impressive creative capabilities, they also raise important safety and ethical risks. These concerns have led to growing interest in concept erasure, the process of removing unwanted concepts from model representations. Existing approaches often achieve strong erasure performance but rely on iterative optimization and may inadvertently distort unrelated concepts. In this work, we present a simple yet principled alternative: a linear transformation framework that achieves concept erasure analytically, without any training. Our method adapts a pretrained model through two sequential, closed-form steps: first, computing a proxy projection of the target concept, and second, applying a constrained transformation within the left null space of known concept directions. This design yields a deterministic and geometrically interpretable procedure for safe, efficient, and theory-grounded concept removal. Across a wide range of experiments, including object and style erasure on multiple Stable Diffusion variants and the flow-matching model (FLUX), our approach matches or surpasses the performance of state-of-the-art methods while preserving non-target concepts more faithfully. Requiring only a few seconds to apply, it offers a lightweight and drop-in tool for controlled model editing, advancing the goal of safer and more responsible generative models.
HSI-GPT2: A Dual-Granularity Large Motion Reasoning Model with Diffusion Refinement for Human-Scene Interaction
PDF ↗Unified interpreting and synthesizing human behaviors within 3D environments is vital for advancing spatial intelligence and humanoid robotics. Despite recent advancements (e.g., HSI-GPT), two fundamental capabilities expected of a unified model--understanding and generation--still lag behind specialist models. This is primarily due to 1) single-granularity codebook overemphasizes low-frequency motion details while neglecting motion semantics, 2) limited decoding capacity of the motion detokenizer which restricts the fidelity of human-scene interactions, 3) only relying on supervised fine-tuning (SFT) failing to capture high-level motion semantics and logical reasoning with an end-to-end mapping. To this issue, we develop HSI-GPT2--a reasoning-enhanced, dual-granularity motion-representational large Scene-Motion-Language model, powered by reinforcement learning (RL) with Chain-of-Thought (CoT) reasoning. First, HSI-GPT2 introduces a Dual-granularity Motion Tokenizer, DMoTok, which jointly preserves both fine-grained motion details and text-aligned motion semantics for various HSI-related tasks. Further, a motion diffusion decoder functions as a motion detokenizer, translating deep semantics and detailed features of LLMs into physically grounded human motions. Finally, we curate a Motion Chain-of-Thought (MoCoT) data engine and extend a Group Relative Policy Optimization (GRPO) paradigm to execute long-horizon and compositionallyrich commands. Results on standard HSI benchmarks confirm the clear superiority of HSI-GPT2 in enhancing interaction quality, semantic alignment, behavioral diversity, and generalization to unseen 3D scenes.
More Natural, More Real: Object-aware Gaussian Splatting for 3D Visual Decoding from Human Brain
PDF ↗Exploring human visual perception and understanding of the stereoscopic world represents a significant topic in computational neuroscience. Recent studies have provided rich Brain-3D datasets, conducted preliminary explorations into 3D visual reconstruction. However, existing research struggles to capture the differences in dynamic changes of 3D stimulus views, and there remains room for improvement in high-fidelity reconstruction and rendering. 3D Gaussian Splatting (3DGS) has recently achieved significant progress in stereoscopic view synthesis. Inspired by it, we propose BrainGS -- an innovative framework for decoding more realistic 3D objects from the brain. BrainGS incorporates a Fusion Time-Spatial Network to achieve comprehensive encoding of the brain, combined with the Multi-Attribute Controller (MAC), it decouples features using visual, semantic, and color as anchors, effectively learning the feature distribution of Brain-3D and providing initial control for 3DGS. The Multi-View Stabilizer (MVS) overcomes the challenge of capturing multi-view changes of 3D objects, creating more robust viewpoint representations. Comprehensive experiments and discussions on fMRI/EEG show the SOTA performance (2.936 FPD, 0.202 LPIPS) of BrainGS, providing reliable neural interpretations, offering new insights into brain stereovision understanding.
While recent Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning abilities, they often produce ungrounded, hallucinated responses by over-relying on linguistic priors rather than visual evidence. This critical limitation arises from the lack of a quantitative measure of how much these models actually rely on visual inputs during reasoning. We propose Draft and Refine (DnR), an agent framework driven by a novel question-conditioned utilization metric. This metric quantifies the model's actual reliance on visual evidence by first constructing a query-conditioned relevance map to localize question-specific evidence, and then assessing dependence through relevance-based probabilistic masking. Guided by this metric, the DnR agent refines its initial "draft" through targeted feedback from external visual experts. Each expert's output (e.g., boxes, masks) is rendered as visual cues on the image, and the LVLM is re-queried to select the response that yields the greatest improvement in utilization. This process strengthens visual grounding of predictions without retraining or architectural changes. Experiments across a broad range of VQA and captioning benchmarks demonstrate consistent accuracy gains and reduced hallucination. These results show that quantifying visual utilization provides a principled path for designing more interpretable and evidence-driven multimodal agent systems that effectively leverage visual experts.
GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
PDF ↗Recent advances in multimodal large language models (MLLMs) have accelerated progress in domain-oriented AI, yet their development in geoscience and remote sensing (RS) remains constrained by distinctive challenges: wide-ranging disciplinary knowledge, heterogeneous sensor modalities, and a fragmented spectrum of tasks. To bridge these gaps, we introduce GeoMMBench, a comprehensive multimodal question-answering benchmark covering diverse RS disciplines, sensors, and tasks, enabling broader and more rigorous evaluation than prior benchmarks. Using GeoMMBench, we assess 36 open-source and proprietary large language models (LLMs), uncovering systematic deficiencies in domain knowledge, perceptual grounding, and reasoning--capabilities essential for expert-level geospatial interpretation. Beyond evaluation, we propose GeoMMAgent, a multi-agent framework that strategically integrates retrieval, perception, and reasoning through domain-specific RS models and tools. Extensive experimental results demonstrate that GeoMMAgent significantly outperforms standalone LLMs, underscoring the importance of tool-augmented agents for dynamically tackling complex geoscience and RS challenges.
Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present **A3 (Advertising Aesthetic Assessment)**, a comprehensive framework encompassing four components: a paradigm (**A3-Law**), a dataset (**A3-Dataset**), a multimodal large language model (**A3-Align**), and a benchmark (**A3-Bench**). Central to A3 is a theory-driven paradigm, A3-Law, comprising three hierarchical stages: (1) Perceptual Attention, evaluating perceptual image signals for their ability to attract attention; (2) Formal Interest, assessing formal composition of image color and spatial layout in evoking interest; and (3) Desire Impact, measuring desire evocation from images and their persuasive impact. Building on A3-Law, we construct A3-Dataset with 120K instruction-response pairs from 30K advertising images, each richly annotated with multi-dimensional labels and Chain-of-Thought (CoT) rationales. We further develop A3-Align, trained under A3-Law with CoT-guided learning on A3-Dataset. Extensive experiments on A3-Bench demonstrate that A3-Align achieves superior alignment with A3-Law compared to existing models, and this alignment generalizes well to quality advertisement selection and prescriptive advertisement critique, indicating its potential for broader deployment. Dataset, code, and models can be found at: https://github.com/euleryuan/A3-Align
Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty. This motivates the need for adaptively balancing data and teacher supervision. We propose Beta-weighted Knowledge Distillation (Beta-KD), an uncertainty-aware distillation framework that adaptively modulates how much the student relies on teacher guidance. Specifically, we formulate teacher-student learning from a unified Bayesian perspective and interpret teacher supervision as a Gibbs prior over student activations. This yields a closed-form, uncertainty-aware weighting mechanism and supports arbitrary distillation objectives and their combinations. Extensive experiments are conducted on multimodal VQA benchmarks by distilling a student Vision-Language Model from a large teacher VLM. The results demonstrate that Beta-KD consistently outperforms existing knowledge distillation methods. Code is available at https://github.com/Jingchensun/beta-kd.
ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning
PDF ↗Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks.We present **ARM-Thinker**, an **A**gentic multimodal **R**eward **M**odel that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring.This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models.We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy.To evaluate agentic reward modeling, we introduce **ARMBench-VL**, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification).ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks.Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.
VideoRealBench: A Chain-of-Thought Realism Evaluation Benchmark for Generated Human-Centric Videos
PDF ↗With the great advancement of video generation models, a growing number of content creators and researchers are leveraging these technologies to produce large volumes of human-centric videos for content creation and customized data generation for specific tasks. Although existing video generation models are capable of producing videos with high visual quality, their inadequate understanding of video realism results in generating unrealistic videos. While various evaluators have emerged to assess the quality of generated videos, they are trained from low-quality generated videos and data annotations, leading to misaligned ratings with human preferences. They also lack interpretability due to the absence of chain-of-thought reasoning. To address these issues, we propose VideoRealBench, a comprehensive benchmark for evaluating the realism of generated human-centric videos. We leverage a rating scale designed from human preferences to score videos and provide three-step rationales, thereby creating a finely-annotated dataset VideoRealDataset and proposing an evaluator VideoRealEval capable of providing reliable scores along with detailed rationales. VideoRealEval achieves a Pearson's linear correlation coefficient (PLCC) of 57.07% and a Spearman's rank correlation coefficient (SROCC) of 56.78% on VideoRealDataset, demonstrating closer alignment with human preferences than existing evaluators. Our code and dataset are available at https://github.com/MCG-NJU/VideoRealBench.
SafeRoPE: Risk-specific Head-wise Embedding Rotation for Safe Generation in Rectified Flow Transformers
PDF ↗Recent Text-to-Image (T2I) models based on rectified-flow transformers (e.g., SD3, FLUX) achieve high generative fidelity but remain vulnerable to unsafe semantics, especially when triggered by multi-token interactions. Existing mitigation methods largely rely on fine-tuning or attention modulation for concept unlearning; however, their expensive computational overhead and design tailored to U-Net-based denoisers hinder direct adaptation to transformer-based diffusion models (e.g., MMDiT). In this paper, we conduct an in-depth analysis of the attention mechanism in MMDiT and find that unsafe semantics concentrate within interpretable, low-dimensional subspaces at head level, where a finite set of safety-critical heads is responsible for unsafe feature extraction. We further observe that perturbing the Rotary Positional Embedding (RoPE) applied to the query and key vectors can effectively modify some specific concepts in the generated images. Motivated by these insights, we propose SafeRoPE, a lightweight and fine-grained safe generation framework for MMDiT. Specifically, SafeRoPE first constructs head-wise unsafe subspaces by decomposing unsafe embeddings within safety-critical heads, and computes a Latent Risk Score (LRS) for each input vector via projection onto these subspaces. We then introduce head-wise RoPE perturbations that can suppress unsafe semantics without degrading benign content or image quality. SafeRoPE combines both head-wise LRS and RoPE perturbations to perform risk-specific head-wise rotation on query and key vector embeddings, enabling precise suppression of unsafe outputs while maintaining generation fidelity. Extensive experiments demonstrate that SafeRoPE achieves SOTA performance in balancing effective harmful content mitigation and utility preservation for safe generation of MMDiT. Codes are available at https://github.com/deng12yx/SafeRoPE.
Text-guided human body animation has advanced rapidly, yet facial animation lags due to the scarcity of well-annotated, text-paired facial corpora. To close this gap, we leverage foundation generative models to synthesize a large, balanced corpus of facial behavior. We design prompts suite covering emotions and head motions, generate about 80 hours of facial videos with multiple generators, and fit per-frame 3D facial parameters, yielding large-scale (prompt and parameter) pairs for training. Building on this dataset, we probe language models for bidirectional competence over facial motion via two complementary tasks: (1) Motion2Language: given a sequence of 3D facial parameters, the model produces natural-language descriptions capturing content, style, and dynamics; and (2) Language2Motion: given a prompt, the model synthesizes the corresponding sequence of 3D facial parameters via quantized motion tokens for downstream animation. Extensive experiments show that in this setting language models can both interpret and synthesize facial motion with strong generalization. To best of our knowledge, this is the first work to cast facial-parameter modeling as a language problem, establishing a unified path for text-conditioned facial animation and motion understanding.
Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the infrared modality is absent, pixel-space generative substitutes become hard to control and inherently lack interpretability. We address missing-IR fusion by proposing a dictionary-guided, coefficient-domain framework built upon a shared convolutional dictionary. The pipeline comprises three key components: (1) Joint Shared-dictionary Representation Learning (JSRL) learns a unified and interpretable atom space shared by both IR and VIS modalities; (2) VIS-Guided IR Inference (VGII) transfers VIS coefficients to pseudo-IR coefficients in the coefficient domain and performs a one-step closed-loop refinement guided by a frozen large language model as a weak semantic prior; and (3) Adaptive Fusion via Representation Inference (AFRI) merges VIS structures and inferred IR cues at the atom level through window attention and convolutional mixing, followed by reconstruction with the shared dictionary. This encode-transfer-fuse-reconstruct pipeline avoids uncontrolled pixel-space generation while ensuring prior preservation within interpretable dictionary-coefficient representation. Experiments under missing-IR settings demonstrate consistent improvements in perceptual quality and downstream detection performance. To our knowledge, this represents the first framework that jointly learns a shared dictionary and performs coefficient-domain inference-fusion to tackle missing-IR fusion. The source code is publicly available at https://github.com/harukiv/DCMIF.
We present Compact 3D Reconstruction with Positive and Negative Primitives (DualPrim), a novel approach for reconstructing compact and topologically regular 3D meshes from multi-view images. Unlike traditional methods that rely on implicit representations such as signed distance functions, or explicit formats such as meshes and point clouds, our method models geometry using quadrics-based 3D primitives. Each primitive is defined by a positive-density superquadric that contributes to the shape, and a negative-density superquadric that carves out local volumes, enabling fine-grained geometric control and flexible topology. This dual-primitive representation yields compact, well-regularized, and efficiently parameterized mesh reconstructions. To infer primitive parameters from multi-view images, we design a differentiable rendering pipeline that jointly estimates positive and negative superquadrics under view-consistent supervision. Extensive experiments demonstrate that DualPrim outperforms state-of-the-art methods in reconstruction accuracy while producing more geometrically concise, interpretable, and high-fidelity 3D meshes.
Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision
PDF ↗Deepfake detection models often generate natural-language explanations, yet their reasoning is frequently ungrounded in visual evidence, limiting reliability. Existing evaluations measure classification accuracy but overlook reasoning fidelity. We propose DeepfakeJudge, a framework for scalable reasoning supervision and evaluation, that integrates an out-of-distribution benchmark containing recent generative and editing forgeries, a human-annotated subset with visual reasoning labels, and a suite of evaluation models, that specialize in evaluating reasoning rationales without the need for explicit ground truth reasoning rationales. The Judge is optimized through a bootstrapped generator-evaluator process that scales human feedback into structured reasoning supervision and supports both pointwise and pairwise evaluation. On the proposed meta-evaluation benchmark, our reasoning-bootstrapped model achieves an accuracy of 96.2%, outperforming \texttt 30x larger baselines. The reasoning judge attains very high correlation with human ratings and 98.9% percent pairwise agreement on the human annotated meta-evaluation subset. These results establish reasoning fidelity as a quantifiable dimension of deepfake detection and demonstrate scalable supervision for interpretable deepfake reasoning. Our user study indicates that humans prefer reasonings generated by our framework 70% of the time, in faithfullness, groundedness and usefulness compared to other models and datasets. All of our datasets, models, and codebase will be open-sourced.
A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that learns a highly compressed two-token state representation using a lightweight encoder and a pre-trained Diffusion Transformer (DiT) decoder, capitalizing on its strong generative prior. Our representation is efficient, interpretable, and integrates seamlessly into existing VLA-based models, improving performance by 11.6% on LIBERO and 31% in real-world task success with minimal inference overhead. More importantly, we find that the difference between these tokens, obtained via latent interpolation, naturally serves as a highly effective latent action, which can be further decoded into executable robot actions. This emergent capability reveals that our representation captures structured dynamics without explicit supervision. We name our method StaMo for its ability to learn generalizable robotic Motion from compact State representation, which is encoded from static images, challenging the prevalent dependence to learning latent action on complex architectures and video data. The resulting latent actions also enhance policy co-training, outperforming prior methods by 10.4% with improved interpretability. Moreover, our approach scales effectively across diverse data sources, including real-world robot data, simulation, and human egocentric video.
Face relighting aims to synthesize realistic portraits under novel illumination while preserving identity and geometry. However, progress remains constrained by the limited availability of large-scale, physically consistent illumination data. To address this, we introduce POLAR, a large-scale and physically calibrated One-Light-at-a-Time (OLAT) dataset containing over 200 subjects captured under 156 lighting directions, multiple views, and diverse expressions. Building upon POLAR, we develop a flow-based generative model POLARNet that predicts per-light OLAT responses from a single portrait, capturing fine-grained and direction-aware illumination effects while preserving facial identity. Unlike diffusion or background-conditioned methods that rely on statistical or contextual cues, our formulation models illumination as a continuous, physically interpretable transformation between lighting states, enabling scalable and controllable relighting. Together, POLAR and POLARNet form a unified illumination learning framework that links real data, generative synthesis, and physically grounded relighting, establishing a self-sustaining "chicken-and-egg" cycle for scalable and reproducible portrait illumination.
Fine-tuning vision-language models such as CLIP has become the mainstream paradigm for multi-label image recognition, and prompt tuning is widely adopted due to its lightweight parameter cost and strong transferability. However, we find that when these methods use Binary Cross-entropy as the supervision loss, the model's confidence structure becomes systematically distorted, leading to pronounced miscalibration. Existing calibration techniques, such as temperature scaling or regularization-based methods, largely fail in multi-label settings because they cannot capture inherent semantic dependencies between classes, nor can they correct the global structural shifts introduced during fine-tuning. To address this issue, we propose Class-wise Covariance Regularization, which aligns the predicted covariance structure of class confidences with the semantic correlations encoded in pretrained text embeddings. This alignment preserves the geometric consistency of the class space throughout fine-tuning, resulting in more stable and interpretable confidence distributions across categories. Experiments on multi-label benchmarks show that CCR significantly reduces calibration errors while maintaining or even improving recognition performance.
VMD-FACT: A New Video Dataset and MLLM-based method for Detecting Realistic AI-Generated Video Misinformation
PDF ↗The rapid evolution of generative AI, including such models as Sora, has intensified the threat of video misinformation. A critical challenge in detecting these AI-generated video misinformation lies in a fundamental disconnect between existing datasets and practical deception tactics. Current datasets often disrupt cross-modal consistency through editing techniques, resulting in unrealistic and easily detectable artifacts. By contrast, generative video misinformation strives for semantic consistency across modalities to remain realism. To address this gap, we introduce RAVM: the first Realistic AI-Generated Video Misinformation Detection Dataset. Unlike existing Video Misinformation Detection (VMD) datasets that are limited to single-source manipulations, RAVM encompasses multiple manipulation sources--Claim, Video, Audio, and Cross-Modal Manipulation--each incorporating diverse manipulation techniques to generate realistic AI-generated video misinformation. To achieve this, we introduce an agent-driven framework for generating realistic video misinformation. Furthermore, we propose an IEEG model that represents multimodal evidence, fact-checking results, and their dependencies as an evidence graph for interpretable detection of AI-generated video misinformation. Extensive experiments on RAVM reveal the vulnerability of existing Multimodal Large Language Models (MLLMs) in detecting AI-generated video misinformation, while the proposed IEEG achieves state-of-the-art performance on RAVM. The dataset is publicly available at: https://gitee.com/VR_NAVE/ravm