GenAI4ELab/papercli-papers huggingface dataset:
CAP: Evaluation of Persuasive and Creative Image Generation
Authors: Aysan Aghazadeh; Adriana Kovashka
Venue: ICCV 2025 (main)

Abstract: We address the task of advertisement image generation and introduce three evaluation metrics to assess Creativity, prompt Alignment, and Persuasiveness (CAP) in generated advertisement images. Despite recent advancements in Text-to-Image (T2I) methods and their performance in generating high-quality images for explicit descriptions, evaluating these models remains challenging. Existing evaluation methods focus largely on assessing alignment with explicit, detailed descriptions, but evaluating alignment with visually implicit prompts remains an open problem. Additionally, creativity and persuasiveness are essential qualities that enhance the effectiveness of advertisement images, yet are seldom measured. To address this, we propose three novel metrics for evaluating the creativity, alignment, and persuasiveness of generated images. We show that current T2I models struggle with creativity, persuasiveness, and alignment when the input text is implicit messages. We further introduce a simple yet effective approach to enhance T2I models' capabilities in producing images that are better aligned, more creative, and more persuasive.
SpatialTrackerV2: Advancing 3D Point Tracking with Explicit Camera Motion
Authors: Yuxi Xiao; Jianyuan Wang; Nan Xue; Nikita Karaev; Yuri Makarov; Bingyi Kang; Xing Zhu; Hujun Bao; Yujun Shen; Xiaowei Zhou
Venue: ICCV 2025 (main)

Abstract: We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos. Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our approach unifies the intrinsic connections between point tracking, monocular depth, and camera pose estimation into a high-performing and feedforward 3D point tracker. It decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end architecture, allowing scalable training across a wide range of datasets, including synthetic sequences, posed RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms existing 3D tracking methods by 30%, and matches the accuracy of leading dynamic 3D reconstruction approaches while running 50x faster.
monoVLN: Bridging the Observation Gap between Monocular and Panoramic Vision and Language Navigation
Authors: Renjie Lu; Yu Zhou; Hao Cheng; Jingke Meng; Wei-Shi Zheng
Venue: ICCV 2025 (main)

Abstract: Vision and Language Navigation(VLN) requires agents to navigate 3D environments by following natural language instructions. While existing methods predominantly assume access to panoramic observations, many practical robotics are equipped with monocular RGBD cameras, creating a significant configuration disparity. In this work, we address this critical gap by developing a novel 3DGS-based framework for monocular VLN agents, focusing on the intrinsic information incompleteness challenge. Our approach incorporates two key innovations: (1) implicit partial completion module for inferring representations of missing regions in incompletely rendered panoramic feature maps, and (2) an uncertainty-aware active perception strategy that enables the agent to actively acquire visual observation when uncertain about its decision. Extensive experiments on R2R-CE and RxR-CE datasets demonstrate that our monoVLN outperforms all existing monocular methods, significantly improve 8% success rate on R2R-CE compared to previous monocular methods. We also validate our monoVLN in real-world environments, providing a practical solution for real-world VLN.
Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP
Authors: Junsung Park; Jungbeom Lee; Jongyoon Song; Sangwon Yu; Dahuin Jung; Sungroh Yoon
Venue: ICCV 2025 (main)

Abstract: While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation -- such as failing to differentiate concepts like "parking" from "no parking" -- poses substantial challenges.By analyzing the data used in the public CLIP model's pre-training, we posit this limitation stems from a lack of negation-inclusive data.To address this, we introduce data generation pipelines that employ a large language model (LLM) and a multimodal LLM to produce negation-inclusive captions.Fine-tuning CLIP with data generated from our pipelines, we develop NegationCLIP, which enhances negation awareness while preserving the generality.Moreover, to enable a comprehensive evaluation of negation understanding, we propose NegRefCOCOg--a benchmark tailored to test VLMs' ability to interpret negation across diverse expressions and positions within a sentence.Experiments on various CLIP architectures validate the effectiveness of our data generation pipelines in enhancing CLIP's ability to perceive negation accurately.Additionally, NegationCLIP's enhanced negation awareness has practical applications across various multimodal tasks, demonstrated by performance gains in text-to-image generation and referring image segmentation.
Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction
Authors: Haonan Wang; Qixiang Zhang; Lehan Wang; Xuanqi Huang; Xiaomeng Li
Venue: ICCV 2025 (main)

Abstract: Decoding visual stimuli from neural activity is essential for understanding the human brain. While fMRI methods have successfully reconstructed static images, fMRI-tovideo reconstruction faces challenges due to the need for capturing spatiotemporal dynamics like motion and scene transitions. Recent approaches have improved semantic and perceptual alignment but struggle to integrate coarse fMRI data with detailed visual features. Inspired by the hierarchical organization of the visual system, we propose NEURONS, a novel framework that decouples learning into four correlated sub-tasks: key object segmentation, concept recognition, scene description, and blurry video reconstruction. This approach simulates the visual cortex's functional specialization, allowing the model to capture diverse video content. In the inference stage, NEURONS generates robust conditioning signals for a pre-trained textto-video diffusion model to reconstruct the videos. Extensive experiments demonstrate that NEURONS outperforms state-of-the-art baselines, achieving solid improvements in video consistency (26.6%) and semantic-level accuracy (19.1%). Notably, NEURONS shows a strong functional correlation with the visual cortex, highlighting its potential for brain-computer interfaces and clinical applications. Code and model weights are available at: https://github.com/xmed-lab/NEURONS.
LACONIC: A 3D Layout Adapter for Controllable Image Creation
Authors: Léopold Maillard; Tom Durand; Adrien Ramanana Rahary; Maks Ovsjanikov
Venue: ICCV 2025 (main)

Abstract: Existing generative approaches for guided image synthesis of multi-object scenes typically rely on 2D controls in the image or text space. As a result, these methods struggle to maintain and respect consistent three-dimensional geometric structure, underlying the scene. In this paper, we propose a novel conditioning approach, training method and adapter network that can be plugged into pretrained text-to-image diffusion models. Our approach provides a way to endow such models with 3D-awareness, while leveraging their rich prior knowledge. Our method supports camera control, conditioning on explicit 3D geometries and, for the first time, accounts for the entire context of a scene, i.e., both on and off-screen items, to synthesize plausible and semantically rich images. Despite its multi-modal nature, our model is lightweight, requires a reasonable number of data for supervised learning and shows remarkable generalization power. We also introduce methods for intuitive and consistent image editing and restyling, e.g., by positioning, rotating or resizing individual objects in a scene. Our method integrates well within various image creation workflows and enables a richer set of applications compared to previous approaches.
InfiniDreamer: Arbitrarily Long Human Motion Generation via Segment Score Distillation
Authors: Wenjie Zhuo; Fan Ma; Hehe Fan
Venue: ICCV 2025 (main)

Abstract: We present InfiniDreamer, a novel framework for generating human motions of arbitrary length. Existing methods typically produce only short sequences, limited by the scarcity of long-range motion data. To address this, InfiniDreamer first generates short sub-motions for each textual description, then coarsely assembles them into a long sequence using randomly initialized transition segments. To refine this coarse motion, we introduce Segment Score Distillation (SSD)---an optimization-based approach that leverages a pre-trained motion diffusion model trained solely on short clips. SSD iteratively refines overlapping short segments sampled from the full sequence, progressively aligning them with the pre-trained short motion prior. This procedure ensures local fidelity within each segment and global consistency across segments. Extensive experiments demonstrate that InfiniDreamer produces coherent, diverse, and context-aware long-range motions without requiring additional long-sequence training.
Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels
Authors: Yujia Tong; Yuze Wang; Jingling Yuan; Chuang Hu
Venue: ICCV 2025 (main)

Abstract: Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing machine unlearning (MU) under data privacy regulations. Existing MU methods designed for full-precision models fail to address two fundamental limitations in quantized networks: 1) Noise amplification from label mismatch during data processing, and 2) Gradient imbalance between forgotten and retained data during training. These issues are exacerbated by quantized models' constrained parameter space and discrete optimization. We propose Q-MUL, the first dedicated unlearning framework for quantized models. Our method introduces two key innovations: 1) Similar Labels assignment replaces random labels with semantically consistent alternatives to minimize noise injection, and 2) Adaptive Gradient Reweighting dynamically aligns parameter update contributions from forgotten and retained data. Through systematic analysis of quantized model vulnerabilities, we establish theoretical foundations for these mechanisms. Extensive evaluations on benchmark datasets demonstrate Q-MUL's superiority over existing approaches.
FairGen: Enhancing Fairness in Text-to-Image Diffusion Models via Self-Discovering Latent Directions
Authors: Yilei Jiang; Wei-Hong Li; Yiyuan Zhang; Minghong Cai; Xiangyu Yue
Venue: ICCV 2025 (main)

Abstract: While Diffusion Models (DM) exhibit remarkable performance across various image generative tasks, they nonetheless reflect the inherent bias presented in the training set.As DMs are now widely used in real-world applications, these biases could perpetuate a distorted worldview and hinder opportunities for minority groups. Existing methods on debiasing DMs usually requires model re-training with a human-crafted reference dataset or additional classifiers, which suffer from two major limitations: (1) collecting reference datasets causes expensive annotation cost; (2) the debiasing performance is heavily constrained by the quality of the reference dataset or the additional classifier. To address the above limitations, we propose FairGen, a plug-and-play method that learns attribute latent directions in a self-discovering manner, thus eliminating the reliance on such reference dataset. Specifically, FairGen consists of two parts: a set of attribute adapters and a distribution indicator. Each adapter in the set aims to learn an attribute latent direction, and is optimized via noise composition through a self-discovering process.Then, the distribution indicator is multiplied by the set of adapters to guide the generation process towards the prescribed distribution. Our method enables debiasing multiple attributes in DMs simultaneously, while remaining lightweight and easily integrable with other DMs, eliminating the need for re-training. Extensive experiments on debiasing gender, racial, and their intersectional biases show that our method outperforms previous SOTA by a large margin.
Gaze-Language Alignment for Zero-Shot Prediction of Visual Search Targets from Human Gaze Scanpaths
Authors: Sounak Mondal; Naveen Sendhilnathan; Ting Zhang; Yue Liu; Michael Proulx; Michael Louis Iuzzolino; Chuan Qin; Tanya R. Jonker
Venue: ICCV 2025 (main)

Abstract: Decoding human intent from eye gaze during a visual search task has become an increasingly important capability within augmented and virtual reality systems. However, gaze target prediction models used within such systems are constrained by the predefined target categories found within available gaze data, limiting their generalizability to novel categories and their usefulness within real-world, interactive systems. In this work, we present the Gaze-Language Alignment Model (GLAM), a vision-language model that can generalize gaze target predictions to novel categories of search targets lacking gaze annotation. To do so, GLAM uses a novel gaze encoder to encode foveal and peripheral information of a gaze scanpath. The resultant gaze embeddings are aligned with language embeddings of large language model-generated search descriptions for associated target categories using a novel contrastive learning strategy called Gaze-Language Alignment Decomposition (GLAD). When used to train GLAM in a zero-shot setup, GLAD surpassed naive contrastive learning strategies by nearly one-third in target prediction accuracy, even outperforming a fully supervised baseline. Moreover, in a fully supervised setup, GLAM outperformed previous methods in target prediction accuracy, regardless of the training strategy used.
ReCoT: Reflective Self-Correction Training for Mitigating Confirmation Bias in Large Vision-Language Models
Authors: Mengxue Qu; Yibo Hu; Kunyang Han; Yunchao Wei; Yao Zhao
Venue: ICCV 2025 (main)

Abstract: Recent advancements in Large Vision-Language Models (LVLMs) have greatly improved their ability to understand both visual and text information. However, a common problem in LVLMs is confirmation bias, where models tend to repeat previous assumptions and follow earlier viewpoints instead of reflecting and correcting themselves. This problem is more common in smaller-scale LVLMs, as they are usually fine-tuned with training data that is mostly positive, focusing on generating coherent dialogue. To address this issue, we introduce ReCoT, a method designed to mitigate confirmation bias in smaller-scale LVLMs through Reflective Self-Correction Training.The method follows a two-stage SFT-DPO paradigm: the first SFT stage aims to cultivate the model's reflective correction abilities, while the DPO stage focuses on enhancing the consistency between answers and reflections. Specifically, we construct dialogue-based reflective samples, which serve as adversarial samples during SFT. In this process, the model is initially presented with a potentially incorrect answer, followed by a reflection and correction phase to generate the final answer. To enhance answer-reflection consistency, we propose the consistency direct preference optimization. To comprehensively evaluate the effectiveness of our ReCoT, we introduce a set of novel metrics to measure the accuracy of the reflection and correction process. Extensive experiments show that ReCoT enables LVLM to engage in robust self-reflection and error correction and reduce confirmation bias.
Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation
Authors: Yuqing Wang; Zhijie Lin; Yao Teng; Yuanzhi Zhu; Shuhuai Ren; Jiashi Feng; Xihui Liu
Venue: ICCV 2025 (main)

Abstract: Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token representation: discrete tokens enable straightforward modeling with standard cross-entropy loss, but suffer from information loss and tokenizer training instability; continuous tokens better preserve visual details, but require complex distribution modeling, complicating the generation pipeline. In this paper, we propose TokenBridge, which bridges this gap by maintaining the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens. To achieve this, we decouple discretization from the tokenizer training process through post-training quantization that directly obtains discrete tokens from continuous representations. Specifically, we introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism that efficiently model the resulting large token space. Extensive experiments show that our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction. This work demonstrates that bridging discrete and continuous paradigms can effectively harness the strengths of both approaches, providing a promising direction for high-quality visual generation with simple autoregressive modeling. Project page: https://yuqingwang1029.github.io/TokenBridge
Trade-offs in Image Generation: How Do Different Dimensions Interact?
Authors: Sicheng Zhang; Binzhu Xie; Zhonghao Yan; Yuli Zhang; Donghao Zhou; Xiaofei Chen; Shi Qiu; Jiaqi Liu; Guoyang Xie; Zhichao Lu
Venue: ICCV 2025 (main)

Abstract: Model performance in text-to-image (T2I) and image-to-image (I2I) generation often depends on multiple aspects, including quality, alignment, diversity, and robustness. However, models' complex trade-offs among these dimensions have been rarely explored due to (1) the lack of datasets that allow fine-grained quantification of these trade-offs, and (2) using a single metric for multiple dimensions. To address this gap, we introduce TRIG-Bench (Trade-offs in Image Generation), which spans 10 dimensions (Realism, Originality, Aesthetics, Content, Relation, Style, Knowledge, Ambiguity, Toxicity and Bias), contains over 40,200 samples, and covers 132 Pairwise Dimensional Subsets. Furthermore, we develop TRIGScore,a VLM-as-judge metric that automatically adapts to various dimensions. Based on this, we evaluate 14 cutting-edge models across T2I and I2I tasks. In addition, we propose the Relation Recognition System and generate the Dimension Trade-off Map (DTM), which visualizes model-specific capability trade-offs. Our experiments demonstrate that DTM consistently provides a comprehensive understanding of the trade-offs between dimensions for each type of generation models. Notably, after fine-tuning on DTM, the model's dimension-specific impact is mitigated, and overall performance is enhanced. Code is available at: https://github.com/fesvhtr/TRIG.
From Easy to Hard: The MIR Benchmark for Progressive Interleaved Multi-Image Reasoning
Authors: Hang Du; Jiayang Zhang; Guoshun Nan; Wendi Deng; Zhenyan Chen; Chenyang Zhang; Wang Xiao; Shan Huang; Yuqi Pan; Tao Qi; Sicong Leng
Venue: ICCV 2025 (main)

Abstract: Multi-image Interleaved Reasoning aims to improve Multimodal Large Language Models' (MLLMs) ability to jointly comprehend and reason across multiple images and their associated textual contexts, introducing unique challenges beyond single-image or non-interleaved multi-image tasks.While current multi-image benchmarks overlook interleaved textual contexts and neglect distinct relationships between individual images and their associated texts, enabling models to reason over multi-image interleaved data may significantly enhance their comprehension of complex scenes and better capture cross-modal correlations.To bridge this gap, we introduce a novel benchmark MIR, requiring joint reasoning over multiple images accompanied by interleaved textual contexts to accurately associate image regions with corresponding texts and logically connect information across images.To enhance MLLMs' ability to comprehend multi-image interleaved data, we introduce reasoning steps for each instance within the benchmark and propose a stage-wise curriculum learning strategy. This strategy follows an "easy to hard" approach, progressively guiding models from simple to complex scenarios, thereby enhancing their ability to handle challenging tasks.Extensive experiments benchmarking multiple MLLMs demonstrate that our method significantly enhances models' reasoning performance on MIR and other established benchmarks, highlighting the challenges current MLLMs face with multi-image interleaved reasoning.We believe that MIR will encourage further research into multi-image interleaved reasoning, facilitating advancements in MLLMs' capability to handle complex inter-modal tasks.
Rethinking Layered Graphic Design Generation with a Top-Down Approach
Authors: Jingye Chen; Zhaowen Wang; Nanxuan Zhao; Li Zhang; Difan Liu; Jimei Yang; Qifeng Chen
Venue: ICCV 2025 (main)

Abstract: Graphic design is crucial for conveying ideas and messages. Designers usually organize their work into objects, backgrounds, and vectorized text layers to simplify editing. However, this workflow demands considerable expertise. With the rise of GenAI methods, an endless supply of high-quality graphic designs in pixel format has become more accessible, though these designs often lack editability. Despite this, non-layered designs still inspire human designers, influencing their choices in layouts and text styles, ultimately guiding the creation of layered designs. Motivated by this observation, we propose Accordion, a graphic design generation framework taking the first attempt to convert AI-generated designs into editable layered designs, meanwhile refining nonsensical AI-generated text with meaningful alternatives guided by user prompts. It is built around a vision language model (VLM) playing distinct roles in three curated stages: (1) reference creation, (2) design planning, and (3) layer generation. For each stage, we design prompts to guide the VLM in executing different tasks. Distinct from existing bottom-up methods (e.g., COLE and Open-COLE) that gradually generate elements to create layered designs, our approach works in a top-down manner by using the visually harmonious reference image as global guidance to decompose each layer. Additionally, it leverages multiple vision experts such as SAM and element removal models to facilitate the creation of graphic layers. We train our method using the in-house graphic design dataset Design39K, augmented with AI-generated design images coupled with refined ground truth created by a customized inpainting model. Experimental results and user studies by designers show that Accordion generates favorable results on the DesignIntention benchmark, including tasks such as text-to-template, adding text to background, and text de-rendering, and also excels in creating design variations.