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Alberto Jaenal, Paula Carbó Cubero, José Araújo, André Mateus

The growing presence of vision-based systems in the physical world comes with a major requirement: highly accurate estimation of the pose, a task typically addressed through methods based on local features. The totality of the available feature-based localization solutions are designed under the assumption of using the same feature for mapping and localization. However, as the implementation provided by each vendor is based on heterogeneous feature extraction algorithms, collaboration between different devices is not straightforward or even not possible. Although there are some alternatives, such as re-extracting the features or reconstructing the image from them, these are impractical or costly to implement in a real pipeline. To overcome this, and inspired in the seminal work Cross-Descriptor [12], we propose Cross-Feature, a method that applies a patch-based training strategy to a simple MLP which projects features to a common embedded space. As a consequence, our proposal allows to establish suitable correspondences between features computed through heterogeneous algorithms, e.g., SIFT [23] and SuperPoint [9]. We experimentally demonstrate the validity of Cross-Feature by evaluating it in tasks as Image Matching, Visual Localization and a new Collaborative Visual Localization and Mapping scenario. We believe this is the first step towards full Visual Localization interoperability. Code and data will be made available.

Yingqi Tang, Zhuoran Xu, Zhaotie Meng, Erkang Cheng

Although end-to-end autonomous driving (E2E-AD) technologies have made significant progress in recent years, there remains an unsatisfactory performance on closed-loop evaluation. The potential of leveraging planning in query design and interaction has not yet been fully explored. In this paper, we introduce a multi-granularity planning query representation that integrates heterogeneous waypoints, including spatial, temporal, and driving-style waypoints across various sampling patterns. It provides additional supervision for trajectory prediction, enhancing precise closed-loop control for the ego vehicle. Additionally, we explicitly utilize the geometric properties of planning trajectories to effectively retrieve relevant image features based on physical locations using deformable attention. By combining these strategies, we propose a novel end-to-end autonomous driving framework, termed HiP-AD, which simultaneously performs perception, prediction, and planning within a unified decoder. HiP-AD enables comprehensive interaction by allowing planning queries to iteratively interact with perception queries in the BEV space while dynamically extracting image features from perspective views. Experiments demonstrate that HiP-AD outperforms all existing end-to-end autonomous driving methods on the closed-loop benchmark Bench2Drive and achieves competitive performance on the real-world dataset nuScenes.

Guanxing Lu, Baoxiong Jia, Puhao Li, Yixin Chen, Ziwei Wang, Yansong Tang, Siyuan Huang

Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have shown prior success, but lack robust geometric information that requires consistent spatial and physical understanding of the three-dimensional world, even pre-trained on internet-scale video sources. To this end, we propose a novel branch of world model named Gaussian World Model (GWM) for robotic manipulation, which reconstructs the future state by inferring the propagation of Gaussian primitives under the effect of robot actions. At its core is a latent Diffusion Transformer (DiT) combined with a 3D variational autoencoder, enabling fine-grained scene-level future state reconstruction with Gaussian Splatting. GWM can not only enhance the visual representation for imitation learning agent by self-supervised future prediction training, but can serve as a neural simulator that supports model-based reinforcement learning. Both simulated and real-world experiments depict that GWM can precisely predict future scenes conditioned on diverse robot actions, and can be further utilized to train policies that outperform the state-of-the-art by impressive margins, showcasing the initial data scaling potential of 3D world model.

Yidi Shao, Mu Huang, Chen Change Loy, Bo Dai

We introduce GausSim, a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that describes continuous piece of matter, accounting for realistic deformations without idealized assumptions. To improve computational efficiency and fidelity, we employ a hierarchical structure that further organizes kernels into CMSs with explicit formulations, enabling a coarse-to-fine simulation approach. This structure significantly reduces computational overhead while preserving detailed dynamics. In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations. To validate our approach, we present a new dataset, READY, containing multi-view videos of real-world elastic deformations. Experimental results demonstrate that GausSim achieves superior performance compared to existing physics-driven baselines, offering a practical and accurate solution for simulating complex dynamic behaviors. Code and model are available at https://www.mmlab-ntu.com/project/gausim/index.html.

Rongjia Zheng, Qing Zhang, Chengjiang Long, Wei-Shi Zheng

Recent methods have shown that pre-trained diffusion models can be fine-tuned to enable generative inverse rendering by learning image-conditioned noise-to-intrinsic mapping. Despite their remarkable progress, they struggle to robustly produce high-quality results as the noise-to-intrinsic paradigm essentially utilizes noisy images with deteriorated structure and appearance for intrinsic prediction, while it is common knowledge that structure and appearance information in an image are crucial for inverse rendering. To address this issue, we present DNF-Intrinsic, a robust yet efficient inverse rendering approach fine-tuned from a pre-trained diffusion model, where we propose to take the source image rather than Gaussian noise as input to directly predict deterministic intrinsic properties via flow matching. Moreover, we design a generative renderer to constrain that the predicted intrinsic properties are physically faithful to the source image. Experiments on both synthetic and real-world datasets show that our method clearly outperforms existing state-of-the-art methods. Our code is available at https://github.com/OnlyZZZZ/DNF-Intrinsic.

Xinwei Long, Kai Tian, Peng Xu, Guoli Jia, Jingxuan Li, Sa Yang, Yihua Shao, Kaiyan Zhang, Che Jiang, Hao Xu 等

Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpose models to continuously evolve via learning deeper expertise. Now is thus the time further to extend the diversity of specialized applications for knowledgeable LLMs, though collecting high quality data with unexpected and informative tasks is challenging. In this paper, we propose to use advertisement (ad) videos as a challenging test-bed to probe the ability of LLMs in perceiving beyond the objective physical content of common visual domain. Our motivation is to take full advantage of the clue-rich and information-dense ad videos' traits, e.g., marketing logic, persuasive strategies, and audience engagement. Our contribution is three-fold: (1) To our knowledge, this is the first attempt to use ad videos with well-designed tasks to evaluate LLMs. We contribute AdsQA, a challenging ad Video QA benchmark derived from 1,544 ad videos with 10,962 clips, totaling 22.7 hours, providing 5 challenging tasks. (2) We propose ReAd-R, a Deepseek-R1 styled RL model that reflects on questions, and generates answers via reward-driven optimization. (3) We benchmark 14 top-tier LLMs on AdsQA, and our ReAd-R achieves the state-of-the-art outperforming strong competitors equipped with long-chain reasoning capabilities by a clear margin.

Jie Zhu, Sungkil Lee

Flare and glare are common nighttime artifacts that degrade image quality and hinder computer vision tasks. Existing synthetic datasets lack physical realism and diversity, while deep learning-based removal methods struggle in complex scenes, posing significant challenges. To address these issues, we introduce the high-quality annotated Physically-Based Flare and Glare (PBFG) dataset and a Flare and Glare Removal Network (FGRNet). PBFG comprises 2,600 flares and 4,000 glares using our computational rendering scheme with diverse lens systems and optical configurations. Our advanced streak synthesis enhances template fidelity and improves streak removal accuracy. FGRNet leverages spatial-frequency features for comprehensive local and global feature extraction. It introduces a Spatial-Frequency Enhanced Module with a Spatial Reconstruction Unit and a Frequency-Enhanced Unit to extract multi-scale spatial information and enhance frequency representation. This design effectively removes complex artifacts, including large-area glares, diverse flares, and multiple or off-screen-induced streaks. Additionally, a histogram-matching module ensures stylistic and visual consistency with ground truth. Extensive experiments confirm that PBFG accurately replicates real-world patterns, and FGRNet outperforms state-of-the-art methods both quantitatively and qualitatively, resulting in significant gains of PSNRs (up to 2.3 dB and 3.14 dB in an image and its glare regions, respectively).

Xiaoxue Chen, Bhargav Chandaka, Chih-Hao Lin, Ya-Qin Zhang, David Forsyth, Hao Zhao, Shenlong Wang

We present InvRGB+L, a novel inverse rendering model that reconstructs large, relightable, and dynamic scenes from a single RGB+LiDAR sequence. Conventional inverse graphics methods rely primarily on RGB observations and use LiDAR mainly for geometric information, often resulting in suboptimal material estimates due to visible light interference. We find that LiDAR's intensity values--captured with active illumination in a different spectral range--offer complementary cues for robust material estimation under variable lighting. Inspired by this, InvRGB+L leverages LiDAR intensity cues to overcome challenges inherent in RGB-centric inverse graphics through two key innovations: (1) a novel physics-based LiDAR shading model and (2) RGB-LiDAR material consistency losses. The model produces novel-view RGB and LiDAR renderings of urban and indoor scenes and supports relighting, night simulations, and dynamic object insertions--achieving results that surpass current state-of-the-art methods in both scene-level urban inverse rendering and LiDAR simulation.

Wenhang Ge, Jiantao Lin, Guibao Shen, Jiawei Feng, Tao Hu, Xinli Xu, Ying-Cong Chen

We propose PRM, a novel photometric stereo based large reconstruction model to reconstruct high-quality meshes with fine-grained details. Previous large reconstruction models typically prepare training images under fixed and simple lighting, offering minimal photometric cues for precise reconstruction. Furthermore, images containing specular surfaces are treated as out-of-distribution samples, resulting in degraded reconstruction quality. To handle these challenges, PRM renders images by varying materials and lighting, which not only improves the local details by providing rich photometric cues but also increases the model's robustness to variations in the appearance of input images. To offer enhanced flexibility, we incorporate a real-time physically-based rendering (PBR) method and mesh rasterization for ground-truth rendering. By using an explicit mesh as 3D representation, PRM ensures the application of differentiable PBR for predicted rendering. This approach models specular color more accurately for images with varying materials and illumination than previous neural rendering methods and supports multiple supervisions for geometry optimization. Extensive experiments demonstrate that PRM significantly outperforms other models.

Sicong Du, Jiarun Liu, Qifeng Chen, Hao-Xiang Chen, Tai-Jiang Mu, Sheng Yang

A single-pass driving clip frequently results in incomplete scanning of the road structure, making reconstructed scene expanding a critical requirement for sensor simulators to effectively regress driving actions. Although contemporary 3D Gaussian Splatting (3DGS) techniques achieve remarkable reconstruction quality, their direct extension through the integration of diffusion priors often introduces cumulative physical inconsistencies and compromises training efficiency. To address these limitations, we present RGE-GS, a novel expansive reconstruction framework that synergizes diffusion-based generation with reward-guided Gaussian integration. The RGE-GS framework incorporates two key innovations: First, we propose a reward network that learns to identify and prioritize consistently generated patterns prior to reconstruction phases, thereby enabling selective retention of diffusion outputs for spatial stability. Second, during the reconstruction process, we devise a differentiated training strategy that automatically adjust Gaussian optimization progress according to scene converge metrics, which achieving better convergence than baseline methods. Extensive evaluations of publicly available datasets demonstrate that RGE-GS achieves state-of-the-art performance in reconstruction quality.

Tim Seizinger, Florin-Alexandru Vasluianu, Marcos V. Conde, Zongwei Wu, Radu Timofte

Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh with controllable strength remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehlicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehlicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. Our code and data will be public.

Mohamed El Amine Boudjoghra, Ivan Laptev, Angela Dai

With the growing ease of capture of real-world 3D scenes, effective editing becomes essential for the use of captured 3D scan data in various graphics applications.We present ScanEdit, which enables functional editing of complex, real-world 3D scans from natural language text prompts.By leveraging the high-level reasoning capabilities of large language models (LLMs), we construct a hierarchical scene graph representation for an input 3D scan given its instance decomposition. We develop a hierarchically-guided, multi-stage prompting approach using LLMs to decompose general language instructions (that can be vague, without referencing specific objects) into specific, actionable constraints that are applied to our scene graph. Our scene optimization integrates LLM-guided constraints along with 3D-based physical plausibility objectives, enabling the generation of edited scenes that align with a variety of input prompts, from abstract, functional-based goals to more detailed, specific instructions. This establishes a foundation for intuitive, text-driven 3D scene editing in real-world scenes.

Zhen Wu, Jiaman Li, Pei Xu, C. Karen Liu

Intelligent agents must autonomously interact with the environments to perform daily tasks based on human-level instructions. They need a foundational understanding of the world to accurately interpret these instructions, along with precise low-level movement and interaction skills to execute the derived actions. In this work, we propose the first complete system for synthesizing physically plausible, long-horizon human-object interactions for object manipulation in contextual environments, driven by human-level instructions. We leverage large language models (LLMs) to interpret the input instructions into detailed execution plans. Unlike prior work, our system is capable of generating detailed finger-object interactions, in seamless coordination with full-body movements. We also train a policy to track generated motions in physics simulation via reinforcement learning (RL) to ensure physical plausibility of the motion. Our experiments demonstrate the effectiveness of our system in synthesizing realistic interactions with diverse objects in complex environments, highlighting its significant potential for real-world applications.

Shunya Nagashima, Komei Sugiura

Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often lack representation learning from solar images. On the other hand, end-to-end learning approaches struggle to model long-range temporal dependencies in solar images.In this study, we propose Deep Space Weather Model (Deep SWM), which is based on multiple deep state space models for handling both ten-channel solar images and long-range spatio-temporal dependencies. Deep SWM also features a sparse masked autoencoder, a novel pretraining strategy that employs a two-phase masking approach to preserve crucial regions such as sunspots while compressing spatial information.Furthermore, we built FlareBench, a new public benchmark for solar flare prediction covering a full 11-year solar activity cycle, to validate our method.Our method outperformed baseline methods and even human expert performance on standard metrics in terms of performance and reliability. The project page can be found at https://keio-smilab25.github.io/DeepSWM.

Xiaomeng Chu, Jiajun Deng, Guoliang You, Wei Liu, Xingchen Li, Jianmin Ji, Yanyong Zhang

Flexible instruction-guided 6-DoF grasping is a significant yet challenging task for real-world robotic systems. Existing methods utilize the contextual understanding capabilities of the large language models (LLMs) to establish mappings between expressions and targets, allowing robots to comprehend users' intentions in the instructions. However, the LLM's knowledge about objects' physical properties remains underexplored despite its tight relevance to grasping. In this work, we propose GraspCoT, a 6-DoF grasp detection framework that integrates a Chain-of-Thought (CoT) reasoning mechanism oriented to physical properties, guided by auxiliary question-answering (QA) tasks. Particularly, we design a set of QA templates to enable hierarchical reasoning that includes three stages: target parsing, physical property analysis, and grasp action selection. Moreover, GraspCoT presents a unified multimodal LLM architecture, which encodes multi-view observations of 3D scenes into 3D-aware visual tokens, and then jointly embeds these visual tokens with CoT-derived textual tokens within LLMs to generate grasp pose predictions. Furthermore, we present IntentGrasp, a large-scale benchmark that fills the gap in public datasets for multi-object grasp detection under diverse and indirect verbal commands. Extensive experiments on IntentGrasp demonstrate the superiority of our method, with additional validation in real-world robotic applications confirming its practicality. The code is available at https://github.com/cxmomo/GraspCoT.

Anjun Hu, Richard Tomsett, Valentin Gourmet, Massimo Camplani, Jas Kandola, Hanting Xie

We present MiDSummer, a two-stage framework for generating immersive Gaussian Splatting scenes that leverages multiple diffusion guidance signals to enable structured layout control, enhanced physical realism, and improved visual quality.While 3D scene generation has seen significant recent advances, current approaches could benefit from: (1) achieving precise, reliable layout control while preserving open-world generalization and physical plausibility, (2) balancing high-level semantic reasoning with low-level, directly controllable geometric constraints, and (3) effectively utilizing layout knowledge for visual refinement. Our work addresses these challenges through a structured two-stage planning-assembly framework. For planning, we introduce a dual layout diffusion guidance approach to bridge semantic reasoning and geometric controllability. Our approach uniquely integrates LLMs' open-vocabulary reasoning with Graph Diffusion Models' (GDM) geometric precision by incorporating multi-level self-consistency scores over scene graph structures and layout bounding box parameters. This fusion enables fine-grained control over scene composition while ensuring physical plausibility and faithful prompt interpretation. For assembly, we propose a layout-guided optimization technique for scene refinement. We effectively incorporate layout priors obtained during the planning stage into a Stable Diffusion (SD)-based refinement process that jointly optimizes camera trajectories and scene splats. This layout-aware joint optimization, constrained by multi-view consistency, produces visually compelling immersive scenes that are structurally coherent and controllable.

Emily Yue-Ting Jia, Jiageng Mao, Zhiyuan Gao, Yajie Zhao, Yue Wang

Humans possess an exceptional ability to imagine 4D scenes, encompassing both motion and 3D geometry, from a single still image. This ability is rooted in our accumulated observations of similar scenes and an intuitive understanding of physics. In this paper, we aim to replicate this capacity in neural networks, specifically focusing on natural fluid imagery. Existing methods for this task typically employ simplistic 2D motion estimators to animate the image, leading to motion predictions that often defy physical principles, resulting in unrealistic animations. Our approach introduces a novel method for generating 4D scenes with physics-consistent animation from a single image. We propose the use of a physics-informed neural network that predicts motion for each surface point, guided by a loss term derived from fundamental physical principles, including the Navier-Stokes equations. To capture appearance, we predict feature-based 3D Gaussians from the input image and its estimated depth, which are then animated using the predicted motions and rendered from any desired camera perspective. Experimental results highlight the effectiveness of our method in producing physically plausible animations, showcasing significant performance improvements over existing methods. Our project page is \href https://physfluid.github.io/ https://physfluid.github.io/ .

Jeongyun Kim, Seunghoon Jeong, Giseop Kim, Myung-Hwan Jeon, Eunji Jun, Ayoung Kim

Understanding the 3D geometry of transparent objects from RGB images is challenging due to their inherent physical properties, such as reflection and refraction. To address these difficulties, especially in scenarios with sparse views and dynamic environments, we introduce TRAN-D, a novel 2D Gaussian Splatting-based depth reconstruction method for transparent objects. Our key insight lies in separating transparent objects from the background, enabling focused optimization of Gaussians corresponding to the object. We mitigate artifacts with an object-aware loss that places Gaussians in obscured regions, ensuring coverage of invisible surfaces while reducing overfitting. Furthermore, we incorporate a physics-based simulation that refines the reconstruction in just a few seconds, effectively handling object removal and chain-reaction movement of remaining objects without the need for rescanning. TRAN-D is evaluated on both synthetic and real-world sequences, and it consistently demonstrated robust improvements over existing GS-based state-of-the-art methods. In comparison with baselines, TRAN-D reduces the mean absolute error by over 39% for the synthetic TRansPose sequences. Furthermore, despite being updated using only one image, TRAN-D reaches a d < 2.5 cm accuracy of 48.46%, over 1.5 times that of baselines, which uses six images. Code and more results are available at https://jeongyun0609.github.io/TRAN-D/.

Haiyang Bai, Jiaqi Zhu, Songru Jiang, Wei Huang, Tao Lu, Yuanqi Li, Jie Guo, Runze Fu, Yanwen Guo, Lijun Chen

We propose a 3D Gaussian splatting-based framework for outdoor relighting that leverages intrinsic image decomposition to precisely integrate sunlight, sky radiance, and indirect lighting from unconstrained photo collections. Unlike prior methods that compress the per-image global illumination into a single latent vector, our approach enables simultaneously diverse shading manipulation and the generation of dynamic shadow effects. This is achieved through three key innovations: (1) a residual-based sun visibility extraction method to accurately separate direct sunlight effects, (2) a region-based supervision framework with a structural consistency loss for physically interpretable and coherent illumination decomposition, and (3) a ray-tracing-based technique for realistic shadow simulation. Extensive experiments demonstrate that our framework synthesizes novel views with competitive fidelity against state-of-the-art relighting solutions and produces more natural and multifaceted illumination and shadow effects.

Yuan Wang, Yuxin Chen, Zhongang Qi, Lijun Liu, Jile Jiao, Xuetao Feng, Yujia Liang, Ying Shan, Zhipeng Zhang

3D vision-language (3D-VL) reasoning, connecting natural language with 3D physical world, represents a milestone in advancing spatial intelligence. While transformer-based methods dominate 3D-VL research, their quadratic complexity and simplistic positional embedding mechanisms severely limits effective modeling of long-range 3D-VL dependencies and spatial relationships in 3D-VL tasks. State Space Models (SSM) have emerged as promising linear-complexity alternatives for sequential data processing, while inherent selection mechanism offers notable capability for spatial modeling. Despite its potential, straightforward adoption of Mamba to 3D-VL tasks encounters two obstacles: (1) how to perceive the position of 3D objects and understand complex spatial relationships, and (2) how to achieve thorough synergies of multi-modal features. In this paper, we propose Mamba-3VL, a pioneering 3D-VL framework to model complex intra- and inter-modality correlations and enhance spatial relation reasoning, while guaranteeing top-tier performance, high efficiency, and generalization potential for 3D-VL tasks. Specifically, Mamba Mixer explicitly models 3D-VL interaction via channel twisting and relation-prioritized spatial scanning policy. It maximally retain spatial relation of object-centric features. To further provide precise spatial encoding for mamba, we develop Instance-aware Dynamic Position Adapter (IDPA) to dynamically adjust instance-specific positional embeddings and enhance local spatial relation of 3D objects. Extensive results validate Mamba-3VL trumps other competitors on seven 3D-VL benchmarks and showcases versatile potentials for challenging Embodied AI tasks.