Driving view synthesis along free-form trajectories is essential for realistic driving simulations, enabling closed-loop evaluation of end-to-end driving policies. Existing methods excel at view interpolation along recorded paths but struggle to generalize to novel trajectories due to limited viewpoints in driving videos. To tackle this challenge, we propose DriveX, a novel free-form driving view synthesis framework, that progressively distills generative prior into the 3D Gaussian model during its optimization. Within this framework, we utilize a video diffusion model to refine the degraded novel trajectory renderings from the in-training Gaussian model, while the restored videos in turn serve as additional supervision for optimizing the 3D Gaussian. Concretely, we craft an inpainting-based video restoration task, which can disentangle the identification of degraded regions from the generative capability of the diffusion model and remove the need of simulating specific degraded pattern in the training of the diffusion model. To further enhance the consistency and fidelity of generated contents, the pseudo ground truth is progressively updated with gradually improved novel trajectory rendering, allowing both components to co-adapt and reinforce each other while minimizing the disruption on the optimization. By tightly integrating 3D scene representation with generative prior, DriveX achieves high-quality view synthesis beyond recorded trajectories in real time--unlocking new possibilities for flexible and realistic driving simulations on free-form trajectories.
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Metalenses offer significant potential for ultra-compact computational imaging but face challenges from complex optical degradation and computational restoration difficulties. Existing methods typically rely on precise optical calibration or massive paired datasets, which are non-trivial for real-world imaging systems. Furthermore, lack of control over the inference process often results in undesirable hallucinated artifacts. We introduce Degradation-Modeled Multipath Diffusion for tunable metalens photography, leveraging powerful natural image priors from pretrained models instead of large datasets. Our framework uses positive, neutral, and negative-prompt paths to balance high-frequency detail generation, structural fidelity, and suppression of metalens-specific degradation, alongside pseudo data augmentation. A tunable decoder enables controlled trade-offs between fidelity and perceptual quality. Additionally, a spatially varying degradation-aware attention (SVDA) module adaptively models complex optical and sensor-induced degradation. Finally, we design and build a millimeter-scale MetaCamera for real-world validation. Extensive results show that our approach outperforms state-of-the-art methods, achieving high-fidelity and sharp image reconstruction. More materials: https://dmdiff.github.io/.
As large Vision-Language Models (VLMs) gain prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks--techniques that exploit model vulnerabilities to elicit harmful outputs. However, the limited availability of diverse multimodal data has constrained current approaches to rely heavily on adversarial or manually crafted images derived from harmful text datasets, which often lack effectiveness and diversity across different contexts. In this paper, we propose IDEATOR, a novel jailbreak method that autonomously generates malicious image-text pairs for black-box jailbreak attacks. IDEATOR is grounded in the insight that VLMs themselves could serve as powerful red team models for generating multimodal jailbreak prompts. Specifically, IDEATOR leverages a VLM to create targeted jailbreak texts and pairs them with jailbreak images generated by a state-of-the-art diffusion model. Extensive experiments demonstrate IDEATOR's high effectiveness and transferability, achieving a 94% attack success rate (ASR) in jailbreaking MiniGPT-4 with an average of only 5.34 queries, and high ASRs of 82%, 88%, and 75% when transferred to LLaVA, InstructBLIP, and Chameleon, respectively. Building on IDEATOR's strong transferability and automated process, we introduce the VLJailbreakBench, a safety benchmark comprising 3,654 multimodal jailbreak samples. Our benchmark results on 11 recently released VLMs reveal significant gaps in safety alignment. For instance, our challenge set achieves ASRs of 46.31% on GPT-4o and 19.65% on Claude-3.5-Sonnet, underscoring the urgent need for stronger defenses.
Open-vocabulary 3D semantic segmentation has been actively studied by incorporating language features into 3D scene representations.Even though many methods have shown the notable improvement in this task, they still have difficulties to make language embeddings be consistent across different views. This inconsistency highly results in mis-labeling where different language embeddings are assigned to the same part of an object. To address this issue, we propose a simple yet powerful method that aligns language embeddings via the identity information. The key idea is to locate language embeddings for the same identity closely in the latent space while putting them apart otherwise. This approach allows the same object to have identical language embeddings in novel views with accurate semantic masks, which are well aligned with the input text. Furthermore, we propose a progressive mask expanding scheme that enables more accurate extraction of semantic mask boundaries. This scheme is very effective in preserving the boundary shape of the target region by allowing the model to consider the local relationship between segments. Experimental results on benchmark datasets demonstrate that our method delivers state-of-the-art performance in open-vocabulary 3D semantic segmentation.
Who Controls the Authorization? Invertible Networks for Copyright Protection in Text-to-Image Synthesis
PDF ↗To defend against personalized generation, a new form of infringement that is more concealed and destructive, the existing copyright protection methods is to add adversarial perturbations in images. However, these methods focus solely on countering illegal personalization, neglecting the requirement for legitimate personalization. Moreover, none of these methods are capable of directly verifying and tracing the copyright from adversarial examples. In response to these limitations, we propose a traceable and authorizable copyright traceability method that embeds the copyright watermark into images through a series of invertible compound coupling modules. We introduce a novel information exchange mechanism for invertible neural network and design a contrastive learning-based optimization strategy tailored to address personalized infringement issues. Our method effectively mitigates the malicious use of unauthorized personalized generation models by inducing watermark-like artifacts and obscuring privacy details in generated images. Additionally, it facilitates copyright traceability and supports authorized legitimate personalization, thereby offering broader practical applicability. Experimental results demonstrate that our method can almost losslessly restore original image and extract copyright watermark, achieving FID exceeding 300 and causing visually noticeable artifacts in unauthorized personalized images. Furthermore, it exhibits consistent robustness against adversarial purification and text prompt modifications.
Backpropagation-based approaches aim to align diffusion models with reward functions through end-to-end backpropagation of the reward gradient within the denoising chain, offering a promising perspective. However, due to the computational costs and the risk of gradient explosion associated with the lengthy denoising chain, existing approaches struggle to achieve complete gradient backpropagation, leading to suboptimal results. In this paper, we introduce Shortcut-based Fine-Tuning (ShortFT), an efficient fine-tuning strategy that utilizes the shorter denoising chain. More specifically, we employ the recently researched trajectory-preserving few-step diffusion model, which enables a shortcut over the original denoising chain, and construct a shortcut-based denoising chain of shorter length. The optimization on this chain notably enhances the efficiency and effectiveness of fine-tuning the foundational model. Our method has been rigorously tested and can be effectively applied to various reward functions, significantly improving alignment performance and surpassing state-of-the-art alternatives.
Inertial tracking (IT), independent of the environment and external infrastructure, has long been the ideal solution for providing location services to humans. Despite significant strides in inertial tracking empowered by deep learning, prevailing neural inertial tracking predominantly utilizes conventional spatial-temporal features from inertial measurements. Unfortunately, the frequency domain dimension is usually overlooked in the current literature. To this end, in this paper, we propose a Multi-Domain Mixture of Experts model for Neural Inertial Tracking, named M^2EIT. Specifically, M^2EIT first leverages ResNet as a spatial decomposition expert to capture spatial relationships between multivariate timeseries, and State Space Model (SSM)-based Bi-Mamba, the other expert to focus on learning temporal correlations. In the frequency domain mapping, we then introduce the Wavelet-based frequency decomposition expert, which decomposes IMU samples into low-frequency bands and high-frequency bands using the Haar wavelet transform for simulating motion patterns at different temporal scales. To bridge the semantic gap across multiple domains and integrate them adaptively, we design the Multi-Representation Alignment Router (MAR), which consists of a dual cross-domain translation layer, followed by a dynamic router, to achieve multi-domain semantic alignment and optimize expert contributions. Extensive experiments conducted on three real-world datasets demonstrate that the proposed M^2EIT can achieve SOTA results in neural inertial tracking.
We describe a simple method for cross-architecture knowledge distillation, where the knowledge transfer is cast into a redundant information suppression formulation. Existing methods introduce sophisticated modules, architecture-tailored designs, and excessive parameters, which impair their efficiency and applicability. We propose to extract the architecture-agnostic knowledge in heterogeneous representations by reducing the redundant architecture-exclusive information. To this end, we present a simple redundancy suppression distillation (RSD) loss, which comprises cross-architecture invariance maximisation and feature decorrelation objectives. To prevent the student from entirely losing its architecture-specific capabilities, we further design a lightweight module that decouples the RSD objective from the student's internal representations. Our method is devoid of the architecture-specific designs and complex operations in the pioneering method of OFA. It outperforms OFA on CIFAR-100 and ImageNet-1k benchmarks with only a fraction of their parameter overhead, which highlights its potential as a simple and strong baseline to the cross-architecture distillation community.
Video super-resolution (VSR) methods typically exploit information across multiple frames to achieve high quality upscaling, with recent approaches demonstrating impressive performance. Nevertheless, challenges remain, particularly in effectively leveraging information over long distances. To address this limitation in VSR, we propose a strategy for long distance information propagation with a flexible fusion module that can optionally also assimilate information from additional high resolution reference images. We design our overall approach such that it can leverage existing pre-trained VSR backbones and adapt the feature upscaling module to support arbitrary scaling factors. Our experiments demonstrate that we can achieve state-of-theart results on perceptual metrics and deliver more visually pleasing results compared to existing solutions.
Spatial intelligence is emerging as a transformative frontier in AI, yet it remains constrained by the scarcity of large-scale 3D datasets. Unlike the abundant 2D imagery, acquiring 3D data typically requires specialized sensors and laborious annotation. In this work, we present a scalable pipeline that converts single-view images into comprehensive, scale- and appearance-realistic 3D representations -- including point clouds, camera poses, depth maps, and pseudo-RGBD -- via integrated depth estimation, camera calibration, and scale calibration. Our method bridges the gap between the vast repository of imagery and the increasing demand for spatial scene understanding. By automatically generating authentic, scale-aware 3D data from images, we significantly reduce data collection costs and open new avenues for advancing spatial intelligence. We release two generated spatial datasets, i.e., COCO-3D and Objects365-v2-3D, and demonstrate through extensive experiments that our generated data can benefit various 3D tasks, ranging from fundamental perception to MLLM-based reasoning. These results validate our pipeline as an effective solution for developing AI systems capable of perceiving, understanding, and interacting with physical environments.
Collecting real-world data for rare high-risk scenarios, long-tailed driving events, and complex interactions remains challenging, leading to poor performance of existing autonomous driving systems in these critical situations. In this paper, we propose RoboTron-Sim that improves real-world driving in critical situations by utilizing simulated hard cases. First, we develop a simulated dataset called Hard-case Augmented Synthetic Scenarios (HASS), which covers 13 high-risk edge-case categories, as well as balanced environmental conditions such as day/night and sunny/rainy. Second, we introduce Scenario-aware Prompt Engineering (SPE) and an Image-to-Ego Encoder (I2E Encoder) to enable multimodal large language models to effectively learn real-world challenging driving skills from HASS, via adapting to environmental deviations and hardware differences between real-world and simulated scenarios. Extensive experiments on nuScenes show that RoboTron-Sim improves driving performance in challenging scenarios by 50%, achieving state-of-the-art results in real-world open-loop planning. Qualitative results further demonstrate the effectiveness of RoboTron-Sim in better managing rare high-risk driving scenarios.
Customized text-to-video generation with pre-trained large-scale models has recently garnered significant attention by focusing on identity and motion consistency. Existing works typically follow the isolated customized paradigm, where the subject identity or motion dynamics are customized exclusively. However, this paradigm completely ignores the intrinsic mutual constraints and synergistic interdependencies between identity and motion, resulting in identity-motion conflicts throughout the generation process that systematically degrade. To address this, we introduce DualReal, a novel framework that employs adaptive joint training to construct interdependencies between dimensions collaboratively. Specifically, DualReal is composed of two units: (1) Dual-aware Adaptation dynamically switches the training step (i.e., identity or motion), learns the current information guided by the frozen dimension prior, and employs a regularization strategy to avoid knowledge leakage; (2) StageBlender Controller leverages the denoising stages and Diffusion Transformer depths to guide different dimensions with adaptive granularity, avoiding conflicts at various stages and ultimately achieving lossless fusion of identity and motion patterns. We constructed a more comprehensive evaluation benchmark than existing methods. The experimental results show that DualReal improves CLIP-I and DINO-I metrics by 21.7% and 31.8% on average, and achieves top performance on nearly all motion metrics. Page: https://wenc-k.github.io/dualreal-customization
We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method reframes the multi-view video generation task as video-to-videos translation, leveraging publicly available image and video diffusion priors. In essence, Reangle-A-Video operates in two stages. (1) Multi-View Motion Learning: An image-to-video diffusion transformer is synchronously fine-tuned in a self-supervised manner to distill view-invariant motion from a set of warped videos. (2) Multi-View Consistent Image-to-Images Translation: The first frame of the input video is warped and inpainted into various camera perspectives under an inference-time cross-view consistency guidance using DUSt3R, generating multi-view consistent starting images. Extensive experiments on static view transport and dynamic camera control show that Reangle-A-Video surpasses existing methods, establishing a new solution for multi-view video generation. We will publicly release our code and data. Anonymous project page: https://anony1anony2.github.io/
Understanding user actions and their possible mistakes is essential for successful operation of task assistants. In this paper, we develop a unified framework for joint temporal action segmentation and error recognition (recognizing when and which type of error happens) in procedural task videos. We propose a Generalized Task Graph (GTG) whose nodes encode correct steps and background (task-irrelevant actions). We then develop a GTG-Video Alignment algorithm (GTG2Vid) to jointly segment videos into actions and detect frames containing errors. Given that it is infeasible to gather many videos and their annotations for different types of errors, we study a framework that only requires normal (error-free) videos during training. More specifically, we leverage large language models (LLMs) to obtain error descriptions and subsequently use video-language models (VLMs) to generate visually-aligned textual features, which we use for error recognition. We then propose an Error Recognition Module (ERM) to recognize the error frames predicted by GTG2Vid using the generated error features. By extensive experiments on two egocentric datasets of EgoPER and CaptainCook4D, we show that our framework outperforms other baselines on action segmentation, error detection and recognition.
The Inter-Intra Modal Measure: A Predictive Lens on Fine-Tuning Outcomes in Vision-Language Models
PDF ↗The fine-tuning of large vision-language foundation models remains an underexplored area, particularly regarding its impact on learning gains and catastrophic forgetting. Inspired by the significance of modality gaps in contrastive dual-encoders, we introduce the Inter-Intra Modal Measure (IIMM)--a predictive metric that quantifies the relationship between intra-modal image embedding similarity and inter-modal misalignment. Through extensive empirical analysis across four state-of-the-art vision-language models and five fine-tuning techniques, we establish a strong linear relationship: tasks with higher IIMM scores yield greater in-domain performance improvements but suffer from more pronounced out-of-domain degradation, with some parameter-efficient fine-tuning (PEFT) methods exhibiting severe forgetting. Compared to existing transferability measures, the IIMM demonstrates significantly stronger predictive power for accuracy changes post fine-tuning in dual-encoder models. Moreover, we provide a theoretical bound, proving that changes in IIMM are limited by the Wasserstein distance between pre- and post-fine-tuning embedding distributions, ensuring its stability and robustness as a predictive measure. With only a single forward pass of the target data, practitioners can leverage this key insight to evaluate the degree to which a model can be expected to improve following fine-tuning. When combined with prior knowledge of a model's performance across diverse tasks, the IIMM further enhances transferability predictions for novel tasks, offering a lightweight yet effective tool for guiding model adaptation strategies. Our code is provided at https://github.com/mit-ll/IIMM.
MDP-Omni: Parameter-free Multimodal Depth Prior-based Sampling for Omnidirectional Stereo Matching
PDF ↗Omnidirectional stereo matching (OSM) estimates 360deg depth by performing stereo matching on multi-view fisheye images. Existing methods assume a unimodal depth distribution, matching each pixel to a single object. However, this assumption constrains the sampling range, causing over-smoothed depth artifacts, especially at object boundaries. To address these limitations, we propose MDP-Omni, a novel OSM network that leverages parameter-free multimodal depth priors. Specifically, we design a sampling strategy that adaptively adjusts the sampling range based on a multimodal probability distribution, without introducing any additional parameters. Furthermore, we present the azimuth-based multi-view volume fusion module to build a single cost volume. It mitigates false matches caused by occlusions in warped multi-view volumes. Experimental results demonstrate that MDP-Omni significantly improves existing methods, particularly in capturing fine details.
The task of generating 3D facial expressions given various situational contexts is important for applications such as virtual avatars or human-robot interactions. The task is, however, challenging not only because it requires a comprehensive understanding of emotion, expression and contexts, but also there rarely are datasets to support the task. We propose ContextFace, a Multi-modal Large Language Model (MLLM) fine-tuned to generate 3D facial expressions depending on complex situational contexts. To overcome the lack of datasets, we perform a context augmentation to existing emotion recognition datasets; we generate plausible situations and quotes from images and emotions to annotate the dataset. Next, we perform visual instruction tuning of MLLMs on context-augmented datasets to boost their capability of visual synthesis from emotions. Experiments show a superior performance of ContextFace in the zero-shot evaluation of contextual emotion recognition. A qualitative evaluation shows that our method generates expressions consistent with diverse contexts and performs complex emotion reasoning, e.g., speculative generation of expressions of occluded faces through interactive prompting.
OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection
PDF ↗Open-vocabulary 3D object detection for autonomous driving aims to detect novel objects beyond the predefined training label sets in point cloud scenes. Existing approaches achieve this by connecting traditional 3D object detectors with vision-language models (VLMs) to regress 3D bounding boxes for novel objects and perform open-vocabulary classification through cross-modal alignment between 3D and 2D features. However, achieving robust cross-modal alignment remains a challenge due to semantic inconsistencies when generating corresponding 3D and 2D feature pairs. To overcome this challenge, we present OV-SCAN, an Open-Vocabulary 3D framework that enforces Semantically Consistent Alignment for Novel object discovery. OV-SCAN employs two core strategies: discovering precise 3D annotations and filtering out low-quality or corrupted alignment pairs (arising from 3D annotation, occlusion-induced, or resolution-induced noise). Extensive experiments on the nuScenes dataset demonstrate that OV-SCAN achieves state-of-the-art performance.
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
PDF ↗Person re-identification (re-id) models are vital in security surveillance systems, requiring transferable adversarial attacks to explore the vulnerabilities of them. Recently, vision-language models (VLM) based attacks have shown superior transferability by attacking generalized image and textual features of VLM, but they lack comprehensive feature disruption due to the overemphasis on discriminative semantics in integral representation. In this paper, we introduce the Attribute-aware Prompt Attack (AP-Attack), a novel method that leverages VLM's image-text alignment capability to explicitly disrupt fine-grained semantic features of pedestrian images by destroying attribute-specific textual embeddings. To obtain personalized textual descriptions for individual attributes, textual inversion networks are designed to map pedestrian images to pseudo tokens that represent semantic embeddings, trained in the contrastive learning manner with images and a predefined prompt template that explicitly describes the pedestrian attributes. Inverted benign and adversarial fine-grained textual semantics facilitate attacker in effectively conducting thorough disruptions, enhancing the transferability of adversarial examples. Extensive experiments show that AP-Attack achieves state-of-the-art transferability, significantly outperforming previous methods by 22.9% on mean Drop Rate in cross-model&dataset attack scenarios.
Multimodal Large Language Models (MLLMs) have demonstrated significant success in visual understanding tasks. However, challenges persist in adapting these models for video comprehension due to the large volume of data and temporal complexity. Existing Video-LLMs using uniform frame sampling often struggle to capture the query-related crucial spatiotemporal clues of videos effectively. In this paper, we introduce Q-Frame, a novel approach for adaptive frame selection and multi-resolution scaling tailored to the video's content and the specific query. Q-Frame employs a training-free, plug-and-play strategy generated by a text-image matching network like CLIP, utilizing the Gumbel-Max trick for efficient frame selection. Q-Frame allows Video-LLMs to process more frames without exceeding computational limits, thereby preserving critical temporal and spatial information. We demonstrate Q-Frame's effectiveness through extensive experiments on benchmark datasets, including MLVU, LongVideoBench, and Video-MME, illustrating its superiority over existing methods and its applicability across various video understanding tasks.