GenAI4ELab/papercli-papers huggingface dataset:
Multiple Object Tracking as ID Prediction
Authors: Ruopeng Gao; Ji Qi; Limin Wang
Venue: CVPR 2025 (main)

Abstract: Multi-Object Tracking (MOT) has been a long-standing challenge in video understanding. A natural and intuitive approach is to split this task into two parts: object detection and association. Most mainstream methods employ meticulously crafted heuristic techniques to maintain trajectory information and compute cost matrices for object matching. Although these methods can achieve notable tracking performance, they often require a series of elaborate handcrafted modifications while facing complicated scenarios. We believe that manually assumed priors limit the method's adaptability and flexibility in learning optimal tracking capabilities from domain-specific data. Therefore, we introduce a new perspective that treats Multiple Object Tracking as an in-context ID Prediction task, transforming the aforementioned object association into an end-to-end trainable task. Based on this, we propose a simple yet effective method termed MOTIP. Given a set of trajectories carried with ID information, MOTIP directly decodes the ID labels for current detections to accomplish the association process. Without using tailored or sophisticated architectures, our method achieves state-of-the-art results across multiple benchmarks by solely leveraging object-level features as tracking cues. The simplicity and impressive results of MOTIP leave substantial room for future advancements, thereby making it a promising baseline for subsequent research. Our code and checkpoints are released at https://github.com/MCG-NJU/MOTIP.
AutoPresent: Designing Structured Visuals from Scratch
Authors: Jiaxin Ge; Zora Zhiruo Wang; Xuhui Zhou; Yi-Hao Peng; Sanjay Subramanian; Qinyue Tan; Maarten Sap; Alane Suhr; Daniel Fried; Graham Neubig; Trevor Darrell
Venue: CVPR 2025 (main)

Abstract: Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first introduce the SlidesBench benchmark, the first benchmark for slide generation with 7k training and 585 testing examples derived from 310 slide decks across 10 domains. SlidesBench supports evaluations that are (i) reference-based to measure similarity to a target slide, and (ii) reference-free to measure the design quality of generated slides alone. We benchmark end-to-end image generation and program generation methods with a variety of models, and find that programmatic methods produce higher-quality slides in user-interactable formats. Built on the success of program generation, we create AutoPresent, an 8B Llama-based model trained on 7k pairs of instructions paired with code for slide generation, and achieve results comparable to the closed-source model GPT-4o. We further explore iterative design refinement where the model is tasked to self-refine its own output, and we found that this process improves the slide's quality. We hope that our work will provide a basis for future work on generating structured visuals.
SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos
Authors: Yuzheng Liu; Siyan Dong; Shuzhe Wang; Yingda Yin; Yanchao Yang; Qingnan Fan; Baoquan Chen
Venue: CVPR 2025 (main)

Abstract: In this paper, we introduce SLAM3R, a novel and effective system for real-time, high-quality, dense 3D reconstruction using RGB videos. SLAM3R provides an end-to-end solution by seamlessly integrating local 3D reconstruction and global coordinate registration through feed-forward neural networks. Given an input video, the system first converts it into overlapping clips using a sliding window mechanism. Unlike traditional pose optimization-based methods, SLAM3R directly regresses 3D pointmaps from RGB images in each window and progressively aligns and deforms these local pointmaps to create a globally consistent scene reconstruction - all without explicitly solving any camera parameters. Experiments across datasets consistently show that SLAM3R achieves state-of-the-art reconstruction accuracy and completeness while maintaining real-time performance at 20+ FPS. Code available at: https://github.com/PKU-VCL-3DV/SLAM3R.
PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers
Authors: Wooju Lee; Juhye Park; Dasol Hong; Changki Sung; Youngwoo Seo; DongWan Kang; Hyun Myung
Venue: CVPR 2025 (main)

Abstract: Accurate localization is essential for autonomous driving, but GNSS-based methods struggle in challenging environments such as urban canyons. Cross-view pose optimization offers an effective solution for localization by directly estimating vehicle pose using satellite-view images. However, existing methods primarily rely on cross-view features at a given pose, neglecting fine-grained contexts for precision and global contexts for robustness against large initial pose errors. To overcome these limitations, we propose PIDLoc, a novel cross-view pose optimization approach inspired by the proportional-integral-derivative (PID) controller. Using RGB images and LiDAR data, the PIDLoc models cross-view feature relationships through the PID branches and estimates pose via the spatially aware pose estimator (SPE). To enhance localization accuracy, the PID branches leverage feature differences for local context (P), aggregated feature differences for global context (I), and gradients of feature differences for fine-grained context (D). Integrated with the PID branches, the SPE captures spatial relationships within the PID-branch features for consistent localization. Experimental results demonstrate that the PIDLoc achieves state-of-the-art performance in cross-view pose estimation for the KITTI dataset, reducing position error by 37.8% compared with the previous state-of-the-art. Our code is available at https://github.com/url-kaist/PIDLoc.
VisionArena: 230k Real World User-VLM Conversations with Preference Labels
Authors: Christopher Chou; Lisa Dunlap; Koki Mashita; Krishna Mandal; Trevor Darrell; Ion Stoica; Joseph E. Gonzalez; Wei-Lin Chiang
Venue: CVPR 2025 (main)

Abstract: The growing adoption and capabilities of vision-language models (VLMs) demand benchmarks that reflect real-world user interactions. We introduce VisionArena, the largest existing dataset of crowdsourced real-world conversations between users and VLMs. While most visual question-answering datasets focus on close-ended problems or synthetic scenarios, VisionArena contains a wide variety of closed and open ended problems across 230K conversations, 73K unique users, 138 languages, and 45 VLMs. VisionArena consists of VisionArena-Chat, a set of 200k single-turn and multi-turn chat logs with a VLM,  VisionArena-Battle, a set of 30K conversations between a user and 2 anonymous VLMs with preference votes, and VisionArena-Bench, an automatic benchmark consisting of 500 diverse user prompts which can be used to cheaply approximate model rankings.We analyze these datasets and highlight the types of question asked by users, the influence of style on user preference, and areas where models often fall short. We find that more open-ended questions like captioning and humor are heavily influenced by style, which causes certain models like Reka Flash and InternVL which are tuned for style to perform significantly better on these categories compared to other categories. We show that VisionArena-Chat and VisionArena-Battle can be used for post-training to align VLMs to human preferences through supervised fine-tuning. Compared to the popular instruction tuning dataset Llava-Instruct-158K, finetuning the same base model on VisionArena results in a a 17 point improvement on MMMU and a 46 point improvement on the WildVision human preference benchmark. Lastly, we show that running automatic VLM evaluation on VisionArena-Bench results in a model ranking which is largely consistent with major online preference benchmarks. We release both VisionArena and our finetuned model to further VLM development.
FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion
Authors: Haosen Yang; Adrian Bulat; Isma Hadji; Hai X. Pham; Xiatian Zhu; Georgios Tzimiropoulos; Brais Martinez
Venue: CVPR 2025 (main)

Abstract: Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled resolution leads to repetitive patterns and structural distortions.  Retraining at higher resolutions quickly becomes prohibitive. Thus, methods enabling pre-existing diffusion models to operate at flexible test-time resolutions are highly desirable. Previous works suffer from frequent artifacts and often introduce large latency overheads.  We propose two simple modules that combine to solve these issues. We introduce a Frequency Modulation (FM) module that leverages the Fourier domain to improve the global structure consistency, and an Attention Modulation (AM) module which improves the consistency of local texture patterns, a problem largely ignored in prior works. Our method, coined FAM diffusion, can seamlessly integrate into any latent diffusion model and requires no additional training.  Extensive qualitative results highlight the effectiveness of our method in addressing structural and local artifacts, while quantitative results show state-of-the-art performance. Also, our method avoids redundant inference tricks for improved consistency such as patch-based or progressive generation, leading to negligible latency overheads.
DreamOmni: Unified Image Generation and Editing
Authors: Bin Xia; Yuechen Zhang; Jingyao Li; Chengyao Wang; Yitong Wang; Xinglong Wu; Bei Yu; Jiaya Jia
Venue: CVPR 2025 (main)

Abstract: Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster synergistic benefits across different tasks. However, in computer vision, while text-to-image (T2I) models have significantly improved generation quality through scaling up, their framework design did not initially consider how to unify with downstream tasks, such as various types of editing. To address this, we introduce DreamOmni, a unified model for image generation and editing. We begin by analyzing existing frameworks and the requirements of downstream tasks, proposing a unified framework that integrates both T2I models and various editing tasks. Furthermore, another key challenge is the efficient creation of high-quality editing data, particularly for instruction-based and drag-based editing. To this end, we develop a synthetic data pipeline using sticker-like elements to synthesize accurate, high-quality datasets efficiently, which enables editing data scaling up for unified model training.  For training, DreamOmni jointly trains T2I generation and downstream tasks. T2I training enhances the model's understanding of specific concepts and improves generation quality, while editing training helps the model grasp the nuances of the editing task. This collaboration significantly boosts editing performance. Extensive experiments confirm the effectiveness of DreamOmni. The code and model will be released.
Hash3D: Training-free Acceleration for 3D Generation
Authors: Xingyi Yang; Songhua Liu; Xinchao Wang
Venue: CVPR 2025 (main)

Abstract: The quality of 3D generative modeling has been notably improved by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per sepresents a critical problem to efficiency.  In this paper, we introduce Hash3D, a universal acceleration for 3D score distillation sampling (SDS) without model training.Central to Hash3D is the observation that images rendered from similar camera positions and diffusion time-steps often have redundant feature maps. By hashing and reusing these feature maps across nearby timesteps and camera angles, Hash3D eliminates unnecessary calculations. We implement this through an adaptive grid-based hashing. As a result, it largely speeds up the process of 3D generation. Surprisingly, this feature-sharing mechanism not only makes generation faster but also improves the smoothness and view consistency of the synthesized 3D objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models, demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency by 1.5~ 4x. Additionally, Hash3D's integration with 3D Gaussian splatting largely speeds up 3D model creation, reducing text-to-3D conversion to about 10 minutes and image-to-3D conversion to 30 seconds. The project page is https://adamdad.github.io/hash3D/.
SemGeoMo: Dynamic Contextual Human Motion Generation with Semantic and Geometric Guidance
Authors: Peishan Cong; Ziyi Wang; Yuexin Ma; Xiangyu Yue
Venue: CVPR 2025 (main)

Abstract: Generating reasonable and high-quality human interactive motions in a given dynamic environment is crucial for understanding, modeling, transferring, and applying human behaviors to both virtual and physical robots. In this paper, we introduce an effective method, SemGeoMo, for dynamic contextual human motion generation, which fully leverages the text-affordance-joint multi-level semantic and geometric guidance in the generation process, improving the semantic rationality and geometric correctness of generative motions. Our method achieves state-of-the-art performance on three datasets and demonstrates superior generalization capability for diverse interaction scenarios.
MultiGO: Towards Multi-level Geometry Learning for Monocular 3D Textured Human Reconstruction
Authors: Gangjian Zhang; Nanjie Yao; Shunsi Zhang; Hanfeng Zhao; Guoliang Pang; Jian Shu; Hao Wang
Venue: CVPR 2025 (main)

Abstract: This paper investigates the research task of reconstructing the 3D clothed human body from a monocular image. Due to the inherent ambiguity of single-view input, existing approaches leverage pre-trained SMPL(-X) estimation models or generative models to provide auxiliary information for human reconstruction. However, these methods capture only the general human body geometry and overlook specific geometric details, leading to inaccurate skeleton reconstruction, incorrect joint positions, and unclear cloth wrinkles. In response to these issues, we propose a multi-level geometry learning framework. Technically, we design three key components: skeleton-level enhancement, joint-level augmentation, and wrinkle-level refinement modules. Specifically, we effectively integrate the projected 3D Fourier features into a Gaussian reconstruction model, introduce perturbations to improve joint depth estimation during training, and refine the human coarse wrinkles by resembling the de-noising process of the diffusion model. Extensive quantitative and qualitative experiments on two test sets show the superior performance of our approach compared to state-of-the-art (SOTA) methods.
Generative Photomontage
Authors: Sean J. Liu; Nupur Kumari; Ariel Shamir; Jun-Yan Zhu
Venue: CVPR 2025 (main)

Abstract: Text-to-image models are powerful tools for image creation. However, the generation process is akin to a dice roll and makes it difficult to achieve a single image that captures everything a user wants. In this paper, we propose a framework for creating the desired image by compositing it from various parts of generated images, in essence forming a Generative Photomontage. Given a stack of images generated by ControlNet using the same input condition and different seeds, we let users select desired parts from the generated results using a brush stroke interface. We introduce a novel technique that takes in the user's brush strokes, segments the generated images using a graph-based optimization in diffusion feature space, and then composites the segmented regions via a new feature-space blending method. Our method faithfully preserves the user-selected regions while compositing them harmoniously. We demonstrate that our flexible framework can be used for many applications, including generating new appearance combinations, fixing incorrect shapes and artifacts, and improving prompt alignment. We show compelling results for each application and demonstrate that our method outperforms existing image blending methods and various baselines.
Multi-view Reconstruction via SfM-guided Monocular Depth Estimation
Authors: Haoyu Guo; He Zhu; Sida Peng; Haotong Lin; Yunzhi Yan; Tao Xie; Wenguan Wang; Xiaowei Zhou; Hujun Bao
Venue: CVPR 2025 (main)

Abstract: This paper aims to reconstruct the scene geometry from multi-view images with strong robustness and high quality. Previous learning-based methods incorporate neural networks into the multi-view stereo matching and have shown impressive reconstruction results. However, due to the reliance on matching across input images, they typically suffer from high GPU memory consumption and tend to fail in sparse view scenarios. To overcome this problem, we develop a new pipeline, named Murre, for multi-view geometry reconstruction of 3D scenes based on SfM-guided monocular depth estimation. For input images, Murre first recover the SfM point cloud that captures the global scene structure, and then use it to guide a conditional diffusion model to produce multi-view metric depth maps for the final TSDF fusion. By predicting the depth map from a single image, Murre bypasses the multi-view matching step and naturally resolves the issues of previous MVS-based methods. In addition, the diffusion-based model can easily leverage the powerful priors of 2D foundation models, achieving good generalization ability across diverse real-world scenes. To obtain multi-view consistent depth maps, our key design is providing effective guidance on the diffusion model through the SfM point cloud, which is a condensed form of multi-view information, highlighting the scene's salient structure, and can be readily transformed into point maps to drive the image-space estimation process. We evaluate the reconstruction quality of Murre in various types of real-world datasets including indoor, streetscapes, and aerial scenes, surpassing state-of-the-art MVS-based and implicit neural reconstruction-based methods. The code and supplementary materials are available at https://zju3dv.github.io/murre/.
Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing
Authors: Ruiyi Wang; Yushuo Zheng; Zicheng Zhang; Chunyi Li; Shuaicheng Liu; Guangtao Zhai; Xiaohong Liu
Venue: CVPR 2025 (main)

Abstract: Existing real-world image dehazing methods primarily attempt to fine-tune pre-trained models or adapt their inference procedures, thus heavily relying on the pre-trained models and associated training data. Moreover, restoring heavily distorted information under dense haze requires generative diffusion models, whose potential in dehazing remains underutilized partly due to their lengthy sampling processes. To address these limitations, we introduce a novel hazing-dehazing pipeline consisting of a Realistic Hazy Image Generation framework (HazeGen) and a Diffusion-based Dehazing framework (DiffDehaze). Specifically, HazeGen harnesses robust generative diffusion priors of real-world hazy images embedded in a pre-trained text-to-image diffusion model. By employing specialized hybrid training and blended sampling strategies, HazeGen produces realistic and diverse hazy images as high-quality training data for DiffDehaze. To alleviate the inefficiency and fidelity concerns associated with diffusion-based methods, DiffDehaze adopts an Accelerated Fidelity-Preserving Sampling process (AccSamp). The core of AccSamp is the Tiled Statistical Alignment Operation (AlignOp), which can provide a clean and faithful dehazing estimate within a small fraction of sampling steps to reduce complexity and enable effective fidelity guidance. Extensive experiments demonstrate the superior dehazing performance and visual quality of our approach over existing methods. The code is available at https://github.com/ruiyi-w/Learning-Hazing-to-Dehazing.
HuMoCon: Concept Discovery for Human Motion Understanding
Authors: Qihang Fang; Chengcheng Tang; Bugra Tekin; Shugao Ma; Yanchao Yang
Venue: CVPR 2025 (main)

Abstract: We present HuMoCon, a novel motion-video understanding framework designed for advanced human behavior analysis. The core of our method is a human motion concept discovery framework that efficiently trains multi-modal encoders to extract semantically meaningful and generalizable features. HuMoCon addresses key challenges in motion concept discovery for understanding and reasoning, including the lack of explicit multi-modality feature alignment and the loss of high-frequency information in masked autoencoding frameworks. Our approach integrates a feature alignment strategy that leverages video for contextual understanding and motion for fine-grained interaction modeling, further with a velocity reconstruction mechanism to enhance high-frequency feature expression and mitigate temporal over-smoothing. Comprehensive experiments on standard benchmarks demonstrate that HuMoCon enables effective motion concept discovery and significantly outperforms state-of-the-art methods in training large models for human motion understanding.
RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges
Authors: Thibaut Loiseau; Guillaume Bourmaud
Venue: CVPR 2025 (main)

Abstract: Camera pose estimation is crucial for many computer vision applications, yet existing benchmarks offer limited insight into method limitations across different geometric challenges. We introduce RUBIK, a novel benchmark that systematically evaluates image matching methods across well-defined geometric difficulty levels. Using three complementary criteria - overlap, scale ratio, and viewpoint angle - we organize 16.5K image pairs from nuScenes into 33 difficulty levels. Our comprehensive evaluation of 14 methods reveals that while recent detector-free approaches achieve the best performance (>47% success rate), they come with significant computational overhead compared to detector-based methods (150-600ms vs. 40-70ms). Even the best performing method succeeds on only 54.8% of the pairs, highlighting substantial room for improvement, particularly in challenging scenarios combining low overlap, large scale differences, and extreme viewpoint changes. Benchmark will be made publicly available.