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Makanjuola Adekunmi Ogunleye, Eman Abdelrahman, Ismini Lourentzou

Large Language Models are increasingly integrated as the cognitive core of 3D embodied agents to enable complex environmental reasoning. However, these agents tend to inherit the critical flaw of hallucination, often failing to ground their responses to their 3D view. While Visual Contrastive Decoding (VCD) is a powerful training-free method for mitigating hallucinations in 2D image-based models, it has not been adapted to the complex 3D embodied environment. In this paper, we embarked on the ambitious goal of being the very first to bridge this gap by introducing a VCD framework for 3D embodied agents. Our method operates at inference time by generating a "negative" 3D context, not by blurring an image, but by applying novel distortions directly to a 3D scene graph, such as swapping object category labels or noising positional coordinates. We evaluate our approach on standard evaluation benchmarks and find that it consistently outperforms existing models. For example, in the random category of 3D-POPE, our 3D-VCD method reduced Yes-rate from 99.9% to 75.1% while simultaneously increasing precision from 50.0% to 62.2%. These results demonstrate that our training-free approach effectively curbs hallucination, yielding 3D agents that are significantly more reliable and grounded.

Chenhao Zheng, Jieyu Zhang, Jianing Zhang, Weikai Huang, Ashutosh Kumar, Quan Kong, Oncel Tuzel, Chun-Liang Li, Ranjay Krishna

Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video efficiency and scalability. While the recent trajectory-based tokenizers offer a promising solution by decoupling video duration from token count, they rely on complex, external segmentation and tracking pipelines that are slow and task-agnostic. We propose TrajTok, an end-to-end video tokenizer module that is fully integrated and co-trained with video models for a downstream objective, dynamically adapting its token granularity to semantic complexity, independent of video duration. TrajTok contains a unified segmenter that performs implicit clustering over pixels in both space and time to directly produce object trajectories in a single forward pass. By prioritizing downstream adaptability over pixel-perfect segmentation fidelity, TrajTok is lightweight, efficient, and yet empirically improves video understanding performance. With TrajTok, we implement a video CLIP model trained from scratch (TrajViT2). It achieves the best accuracy at scale across both classification and retrieval benchmarks, while maintaining efficiency comparable to the best token-merging methods. TrajTok also proves to be a versatile component beyond its role as a tokenizer. We show that it can be seamlessly integrated as either a probing head for pretrained visual features (TrajAdapter) or an alignment connector in vision-language models (TrajVLM) with especially strong performance in long-video reasoning.

Yan Zhang, Long Ma, Yuxin Feng, Zhe Huang, Fan Zhou, Zhuo Su

Learning-based real image dehazing methods have achieved notable progress, yet they still face adaptation challenges in diverse real haze scenes. These challenges mainly stem from the lack of effective unsupervised mechanisms for unlabeled data and the heavy cost of full model fine-tuning. To address these challenges, we propose the haze-to-clear text-directed loss that leverages CLIP's cross-modal capabilities to reformulate real image dehazing as a semantic alignment problem in latent space, thereby providing explicit unsupervised cross-modal guidance in the absence of reference images. Furthermore, we introduce the Bilevel Layer-positioning LoRA (BiLaLoRA) strategy, which learns both the LoRA parameters and automatically search the injection layers, enabling targeted adaptation of critical network layers. Extensive experiments demonstrate our superiority against state-of-the-art methods on multiple real-world dehazing benchmarks.

Zhengjian Yao, Yongzhi Li, Xinyuan Gao, Quan Chen, Peng Jiang, Yanye Lu

We present Narrative Weaver, a novel framework that addresses a fundamental challenge in generative AI: achieving controllable, long-range, and consistent visual content generation. While existing models excel at generating high-fidelity short-form visual content, they struggle to maintain narrative coherence and visual consistency across extended sequences--a critical limitation for real-world applications such as filmmaking and e-commerce advertising.Narrative Weaver introduces the first holistic solution that seamlessly integrates three essential capabilities: fine-grained control, automatic narrative planning, and long-range coherence. Our architecture combines a Multimodal Large Language Model (MLLM) for high-level narrative planning with a novel fine-grained control module featuring a dynamic Memory Bank that prevents visual drift.To enable practical deployment, we develop a progressive, multi-stage training strategy that efficiently leverages existing pre-trained models, achieving state-of-the-art performance even with limited training data. Recognizing the absence of suitable evaluation benchmarks, we construct and release the E-commerce Advertising Video Storyboard Dataset (EAVSD)--the first comprehensive dataset for this task, containing over 330K high-quality images with rich narrative annotations.Through extensive experiments across three distinct scenarios (controllable multi-scene generation, autonomous storytelling, and e-commerce advertising), we demonstrate our method's superiority while opening new possibilities for AI-driven content creation.

Jianwei Zhao, Fan Yang, Xin Li, Qiang Zhai, Ao Luo, Ziqi Ren, Zhicheng Jiao, Hong Cheng

Understanding how transcriptomic programs shape tissue morphology remains a central challenge in computational pathology. Gene-to-WSI tile synthesis offers a principled generative framework to translate molecular profiles into histological images. However, most existing methods compress RNA-Seq into a single global embedding injected once at initialization, an oversimplified design that weakens transcriptomic signals and induces spurious, non-biological associations. We present GeneVAR, an Autoregressive Gene-to-WSI model that reformulates synthesis as an iterative, coarse-to-fine generative process. At its core is a novel Causal MeanFlow module, which leverages counterfactual interventions to suppress non-biological variations (e.g., staining or contrast artifacts) and enforce an artifact-invariant generation process. Concurrently, it reinforces transcriptome-informed guidance across multiple stages, preserving biological fidelity throughout the generative trajectory. Combined with a b-VAE for compact gene embeddings and a multi-scale vector quantizer for discrete morphology representation, GeneVAR generates H&E-stained WSI tiles that are both visually realistic and transcriptomically faithful. Extensive experiments across five TCGA cancer benchmarks demonstrate consistent state-of-the-art performance, surpassing prior methods in both generative fidelity and downstream classification accuracy.

Yiying Yang, Wei Cheng, Sijin Chen, Honghao Fu, Xianfang Zeng, Yujun Cai, Gang Yu, Xingjun Ma

OmniLottie is a versatile framework that generates high-quality vector animations from multi-modal instructions, including interleaved texts, images, and videos. To fully parameterize vector animations for flexible motion and visual content control, we seek help from the Lottie representation, which encodes both shapes and animated behaviors in a single JSON file. Building upon a pretrained vision-language model (VLM), OmniLottie produces vivid, semantically aligned vector animations that adhere closely to multi-modal conditions. To avoid the complexity and irregularity of raw JSON structures, we introduce a dedicated Lottie tokenizer that transforms Lottie files into structured sequences of function calls representing shapes, animation commands, and their parameters. This design enables the model to directly learn the underlying shape and animation priors from data, substantially improving generation stability and controllability. To further advance research in vector animation generation, we curate MMLottie-2M, a large-scale dataset of professionally designed vector animations paired with textual and visual annotations. Leveraging the well-designed tokenizer and our newly established dataset, OmniLottie demonstrates strong multi-modal conditional generation capabilities using a simple next-token prediction objective. For qualitative results, please refer to the generated animations rendered through standard Lottie players on the supplementary website.

Mingwen Shao, Qiao Zhang, Xinyuan Chen, Xiang Lv, Lingzhuang Meng, Chang Liu, Qinglin Zhan, Ling Jian

Pose-agnostic anomaly detection (PAD) achieves strong performance in localizing anomalies from arbitrary viewpoints when trained on densely sampled normal data. However, under sparse-view conditions, existing methods face two key challenges: (1) sparse observations lead to overfitting and geometric detail loss in 3D reconstruction; (2) limited visual cues lead to inaccurate pose estimation, compromising the reliability of subsequent anomaly localization. To address these challenges, we propose Wave-Pose3D, a wavelet-driven 3D anomaly detection framework tailored for PAD under sparse-view conditions. First, we design a structure-aware and wavelet-optimized Gaussian modeling strategy that dynamically filters unreliable regions via structural priors to mitigate overfitting and leverages high-frequency supervision to restore fine-grained geometric details. Second, to improve pose estimation under sparse views, we develop a wavelet-based pose estimator that integrates low-frequency structural cues and high-frequency details to enhance both initialization and refinement accuracy. Finally, we introduce a wavelet difference-aware anomaly detector that computes frequency-domain anomaly scores, improving localization robustness against pose and geometric variations. By integrating these strategies, Wave-Pose3D achieves robust and accurate anomaly localization under sparse views. Extensive experiments validate that the proposed approach achieves state-of-the-art performance under 10% and 20% sparse-view configurations.

Yu Ma, Zizhan Guo, Zuyi Xiong, Haoran Zhang, Yi Feng, Hongbo Zhao, Hanli Wang, Rui Fan

Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by the absence of metric scale, local inconsistencies, and low computational efficiency. To address these issues, we present Midas Touch for Depth (MTD), a mathematically interpretable approach that converts relative depth into metric depth using only extremely sparse 3D data. To eliminate local scale inconsistencies, it applies a segment-wise recovery strategy via sparse graph optimization, followed by a pixel-wise refinement strategy using a discontinuity-aware geodesic cost. MTD exhibits strong generalization and achieves substantial accuracy improvements over previous depth completion and depth estimation methods. Moreover, its lightweight, plug-and-play design facilitates deployment and integration on diverse downstream 3D tasks.

Ting Yang, Qilong Wang, Qibin Hou, Qinghua Hu

The rise of vision-language models (VLMs) has driven the initial exploration of open-vocabulary remote sensing image semantic segmentation (OVRSIS), enabling recognition of unseen categories in complex Earth observation scenes. However, existing methods primarily focus on enhancing visual representations of domain-specific remote sensing images, while overlooking the effect of textual information. In this paper, we argue that there exists a crucial issue of textual ambiguity in OVRSIS task, limiting final segmentation performance. Therefore, we propose a plug-and-play yet effective Test-time Multi-Prompt Adaptation (TMPA) method to mitigate textual ambiguity in OVRSIS. Specifically, TMPA first generates diverse, context-aware descriptions for each category instead of the naive class name by executing a large language model with a task-driven prompt, which can effectively avoid some textual ambiguity, i.e., background class has different meanings in various tasks. Furthermore, TMPA develops a visual-guided test-time adaptation strategy for the generated multi-prompts, which adaptively refines the prompt representations of each category with high-confidence visual features for the uncertain predictions with high entropy, making TMPA better applicable to different scenarios. Particularly, a pixel-level loss with entropy minimization is proposed to optimize the text prompt with a bias during inference, where prompt bias is constructed based on a weighted combination of high-confidence visual features. Our TMPA can be flexibly integrated into existing methods for boosting their performance. Extensive experiments are conducted on 17 remote sensing datasets, and the results show our TMPA can significantly improve its counterparts, while achieving state-of-the-art performance.

Hao Bai, Alexey Taymanov, Tong Zhang, Aviral Kumar, Spencer Whitehead

We present WebGym, the largest-to-date open-source environment for training realistic visual web agents. Real websites are non-stationary and diverse, making artificial or small-scale task sets insufficient for robust policy learning. WebGym contains nearly 300,000 tasks with rubric-based evaluations across diverse, real-world websites and difficulty levels. We train agents with a simple reinforcement learning (RL) recipe, which trains on the agent's own interaction traces (rollouts), using task rewards as feedback to guide learning. To enable scaling RL, we speed up sampling of trajectories in WebGym by developing a high-throughput asynchronous rollout system, designed specifically for web agents. Our system achieves a 4-5x rollout speedup compared to naive implementations. Second, we scale the task set breadth, depth, and size, which results in continued performance improvement. Fine-tuning a strong base vision-language model, Qwen-3-VL-8B-Instruct, on WebGym results in an improvement in success rate on an out-of-distribution test set from 26.2% to 42.9%, significantly outperforming agents based on proprietary models such as GPT-4o and GPT-5-Thinking that achieve 27.1% and 29.8%, respectively. This improvement is substantial because our test set consists only of tasks on websites never seen during training, unlike many other prior works on training visual web agents.

Haojie Yan, Zehao Chen, Yan Liu, Shi Gu, Peng Lin, De Ma, Huajin Tang, Qian Zheng, Gang Pan

Perception under low illumination remains a major challenge for computer vision systems, as RGB sensors often fail to capture sufficient structural and color information in extremely dark environments. Event cameras, with their high dynamic range and temporal resolution, provide complementary cues that are well suited for such conditions. In this work, we present eRetinexGS, a novel framework that jointly leverages event streams and low-light frames through 3D Gaussian Splatting for scene-level enhancement and reconstruction. Unlike previous approaches that operate on individual frames, eRetinexGS enforces geometric and photometric consistency across multiple views, bridging the gap between degraded images and noisy event signals. By introducing an event-assisted Retinex decomposition and a reflectance-illumination representation within the 3DGS pipeline, our method reconstructs normal-light radiance fields with fine-grained details and accurate color. Extensive experiments on both synthetic and real datasets demonstrate that eRetinexGS achieves state-of-the-art performance in low-light scene enhancement while maintaining real-time rendering capability.

Xingyu Liu, Pengfei Ren, Qi Qi, Haifeng Sun, Zirui Zhuang, Jianxin Liao, Jingyu Wang

Understanding interacting hand-object from monocular videos is crucial for immersive and dexterous interactions in AR/VR and robotic applications. However, existing monocular reconstruction methods primarily assume rigid grasping and static object geometry. When applied to articulated manipulations, the continuous joint rotations and frequent component deformations introduce a strong coupling between shape and motion, leading to severe ambiguity and instability in articulation optimization under monocular observation. To address this challenge, we propose a Clay-to-Stone dual-phase framework, modeling the articulated manipulation at hierarchical granularities, enabling a progression from flexible semantic exploration to structured articulation recovery. In the CLAY phase, our method performs fine-grained control over geometric deformation, guided by inter-part semantic correlation learning. As semantic and motion priors emerge, the STONE phase enforces rigid constraints to consolidate articulated structures and explicitly estimates motion parameters. Experiments on a real-world manipulation dataset show that our method achieves state-of-the-art reconstruction quality and plausible articulation modeling from monocular videos. Code is available at https://github.com/ru1ven/ARGS.

Jin Cui, Boran Zhao, Jiajun Xu, Jiaqi Guo, Shuo Guan, Pengju Ren

Coreset selection compresses large datasets into compact, representative subsets, reducing the energy and computational burden of training deep neural networks. Existing methods are either: (i) DNN-based, which are inherently coupled with network-specific parameters, inevitably introducing architectural bias and compromising generalization; or (ii) DNN-free, which utilize heuristics that lack rigorous theoretical guarantees for stability and accuracy. Neither approach explicitly constrains distributional equivalence of the representative subsets, largely because continuous distribution matching is broadly considered inapplicable to discrete dataset sampling. Furthermore, prevalent distribution metrics (e.g., MSE, KL, MMD, and CE) are often incapable of accurately capturing higher-order moment differences. These deficiencies lead to suboptimal coreset performance, preventing the selected coreset from being truly equivalent to the original dataset.In this work, we propose FAST (Frequency-domain Aligned Sampling via Topology), the first DNN-free distribution-matching coreset selection framework that formulates coreset selection as a graph-constrained optimization problem grounded in spectral graph theory and employs the Characteristic Function Distance (CFD) to capture full distributional information (i.e., all moments and intrinsic correlations) in the frequency domain. We further discover that naive CFD suffers from a "vanishing phase gradient" issue in medium and high-frequency regions; to address this, we introduce an Attenuated Phase-Decoupled CFD. Furthermore, for better convergence, we design a Progressive Discrepancy-Aware Sampling strategy that progressively schedules frequency selection from low to high. This preserves global structures before refining local details, enabling accurate matching with few frequencies while preventing overfitting. Extensive experiments demonstrate that FAST significantly outperforms state-of-the-art coreset selection methods across all evaluated benchmarks, achieving an average accuracy gain of 9.12%. Compared to other baseline coreset methods, it reduces power consumption by 96.57% and achieves a 2.2xaverage speedup even on CPU with 1.7GB of memory, underscoring its high performance and energy efficiency.

Haonan Cai, Yuxuan Luo, Zhouhui Lian

Manual font design is an intricate process that transforms a stylistic visual concept into a coherent glyph set. This challenge persists in automated Few-shot Font Generation (FFG), where models struggle to preserve both structural integrity and stylistic fidelity from limited references. While autoregressive (AR) models have demonstrated impressive generative capabilities, their application to FFG is constrained by conventional patch-level tokenization, which neglects global dependencies crucial for coherent font synthesis. Moreover, existing FFG methods remain within the image-to-image paradigm, relying solely on visual references and overlooking the role of language in conveying stylistic intent during font design. To address these limitations, we propose GAR-Font, a novel AR framework for multimodal few-shot font generation. GAR-Font introduces a global-aware tokenizer that effectively captures both local structures and global stylistic patterns, a multimodal style encoder offering flexible style control through a lightweight language-style adapter without requiring intensive multimodal pretraining, and a post-refinement pipeline that further enhances structural fidelity and style coherence. Extensive experiments show that GAR-Font outperforms existing FFG methods, excelling in maintaining global style faithfulness and achieving higher-quality results with textual stylistic guidance. Project Page: https://xtryer-s.github.io/projects_pages/GAR_Font.

Linqing Wang, Zhiyong Xu, Ximing Xing, Yiji Cheng, Zhiyuan Zhao, Donghao Li, Tiankai Hang, Zhenxi Li, Jiale Tao, Qixun Wang 等

Recent text-to-image (T2I) diffusion models have achieved impressive progress in generating high-fidelity images, yet they often fail to faithfully follow complex user prompts, especially in attribute binding, negation, and compositional reasoning. To address this limitation, we propose PromptEnhancer, a universal prompt rewriting framework that improves prompt interpretability for any pre-trained T2I model. PromptEnhancer is trained through a multi-stage pipeline. We first perform supervised fine-tuning on chain-of-thought-style rewriting data to endow the rewriter with structured prompt analysis and rewriting capabilities. We then introduce AlignEvaluator, a task-specific reward model that provides explicit and fine-grained feedback based on a taxonomy of common T2I failure modes, and further optimize the rewriter with reinforcement learning. This design enables the rewriter to produce prompts that are more precise, semantically complete, and easier for T2I models to follow. To support evaluation, we also construct a comprehensive human-aligned benchmark covering diverse semantic and compositional challenges. Extensive experiments show that PromptEnhancer consistently improves image-text alignment across multiple T2I models and significantly enhances prompt fidelity on challenging cases.

Fukun Yin, Shiyu Liu, Yucheng Han, Zhibo Wang, Peng Xing, Rui Wang, Wei Cheng, Yingming Wang, Aojie Li, Zixin Yin 等

Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion decoder, as seen in systems such as Step1X-Edit and Qwen-Image-Edit, where the MLLM encodes both the reference image and the instruction but remains frozen during training.In this work, we demonstrate that unlocking the reasoning capabilities of MLLM can further push the boundaries of editing models. Specifically, we explore two reasoning mechanisms, thinking and reflection, which enhance instruction understanding and editing accuracy.Based on that, our proposed framework enables image editing in a thinking-editing-reflection loop: the thinking mechanism leverages the world knowledge of MLLM to interpret abstract instructions, while the reflection reviews editing results, automatically corrects unintended manipulations, and identifies the stopping round.Extensive experiments demonstrate that our reasoning approach achieves significant performance gains, with improvements of ImgEdit(+4.4%), GEdit(+3.1%), and Kris(+11.5%) when initializing our DiT from the Step1X-Edit(ReasonEdit-S), and also outperforms previous open-source methods on both GEdit and Kris when integrated with Qwen-Image-Edit(ReasonEdit-Q).Code and checkpoints will be open-sourced.

Xiang Deng, Feng Gao, Yong Zhang, Youxin Pang, Xu Xiaoming, Zhuoliang Kang, Xiaoming Wei, Yebin Liu

Full-stack multimodal interaction in real-time is a central goal in building intelligent embodied agents capable of natural, dynamic communication. However, existing systems are either limited to unimodal generation or suffer from degraded reasoning and poor cross-modal alignment, preventing coherent and perceptually grounded interactions. In this work, we introduce U-Mind, the first unified system for high-intelligence multimodal dialogue that supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop. At its core, U-Mind implements a Unified Alignment and Reasoning Framework that addresses two key challenges: enhancing cross-modal synchronization via a segment-wise alignment strategy, and preserving reasoning abilities through Rehearsal-Driven Learning. During inference, U-Mind adopts a text-first decoding pipeline that performs internal chain-of-thought planning followed by temporally synchronized generation across modalities. To close the loop, we implement a real-time video rendering framework conditioned on pose and speech, enabling expressive and synchronized visual feedback. Extensive experiments demonstrate that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks, including question answering, instruction following, and motion generation, paving the way toward intelligent, immersive conversational agents.

Yujia Wang, Yuyan Li, Jiuming Liu, Fang-Lue Zhang, Xinhu Zheng, Neil.A Dodgson

Blind 360deg image quality assessment (IQA) aims to predict perceptual quality for panoramic images without a pristine reference. Unlike conventional planar images, 360deg content in immersive environments restricts viewers to a limited viewport at any moment, making viewing behaviors critical to quality perception. Although existing scanpath-based approaches have attempted to model viewing behaviors by approximating the human view-then-rate paradigm, they treat scanpath generation and quality assessment as separate steps, preventing end-to-end optimization and task-aligned exploration. To address this limitation, we propose RL-ScanIQA, a reinforcement-learned framework for blind 360deg IQA. RL-ScanIQA optimize a PPO-trained scanpath policy and a quality assessor, where the policy receives quality-driven feedback to learn task-relevant viewing strategies. To improve training stability and prevent mode collapse, we design multi-level rewards, including scanpath diversity and equator-biased priors. We further boost cross-dataset robustness using distortion-space augmentation together with rank-consistent losses that preserve intra-image and inter-image quality orderings. Extensive experiments on three benchmarks show that RL-ScanIQA achieves superior in-dataset performance and cross-dataset generalization.

Romario Gualdrón-Hurtado, Roman Jacome, Rafael S. Suárez, Henry Arguello

Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of the sensing matrix. Common image priors promote solutions on the general image manifold, such as sparsity, smoothness, or score function. However, as these priors do not constrain the null-space component, they can bias the reconstruction. Thus, we aim to incorporate meaningful null-space information in the reconstruction framework. Inspired by smooth image representation on graphs, we propose Graph-Smooth Null-Space Representation (GSNR), a mechanism that imposes structure only into the invisible component. Particularly, given a graph Laplacian, we construct a null-restricted Laplacian that encodes similarity between neighboring pixels in the null-space signal, and we design a low-dimensional projection matrix from the p-smoothest spectral graph modes (lowest graph frequencies). This approach has strong theoretical and practical implications: i) improved convergence via a null-only graph regularizer, ii) better coverage, how much null-space variance is captured by p modes, and iii) high predictability, how well these modes can be inferred from the measurements. GSNR is incorporated into well-known inverse problem solvers, e.g., PnP, DIP, and diffusion solvers, in four scenarios: image deblurring, compressed sensing, demosaicing, and image super-resolution, providing consistent improvement of up to 4.3 dB over baseline formulations and up to 1 dB compared with end-to-end learned models in terms of PSNR.

Jiahao Wang, Bo Sun, Yijing Bai, Vincent Casser, Songyou Peng, Zehao Zhu, Meng-Li Shih, Xander Masotto, Shih-Yang Su, Kanaad Parvate 等

Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage. In contrast, in-the-wild data from sources like dashcams offers immense scale and diversity, capturing critical long-tail scenarios and novel environments. However, this unstructured, in-the-wild video data is incompatible with ADS expecting structured, multi-modal sensor inputs for validation and training. To bridge this data gap, we propose Sensor2Sensor, a novel generative modeling paradigm that translates in-the-wild monocular dashcam videos into a high-fidelity, multi-modal sensor suite (AV logs) comprising multi-view camera images and LiDAR point clouds. A core challenge is the lack of paired training data. We address this by converting real AV logs into dashcam-style videos via 4D Gaussian Splatting (4DGS) reconstruction and novel-view rendering. Sensor2Sensor then utilizes a diffusion architecture to perform the generative conversion. We perform comprehensive quantitative evaluations on the fidelity and realism of the generated sensor data. We demonstrate Sensor2Sensor's practical utility by converting challenging in-the-wild internet and dashcam footage into realistic, multi-modal data formats, further unlocking vast external data sources for AV development.