GenAI4ELab/papercli-papers huggingface dataset:
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding
Authors: Tatiana Zemskova; Dmitry Yudin
Venue: ICCV 2025 (main)

Abstract: A 3D scene graph represents a compact scene model by capturing both the objects present and the semantic relationships between them, making it a promising structure for robotic applications. To effectively interact with users, an embodied intelligent agent should be able to answer a wide range of natural language queries about the surrounding 3D environment. Large Language Models (LLMs) are beneficial solutions for user-robot interaction due to their natural language understanding and reasoning abilities. Recent methods for learning scene representations have shown that adapting these representations to the 3D world can significantly improve the quality of LLM responses. However, existing methods typically rely only on geometric information, such as object coordinates, and overlook the rich semantic relationships between objects. In this work, we propose 3DGraphLLM, a method for constructing a learnable representation of a 3D scene graph that explicitly incorporates semantic relationships. This representation is used as input to LLMs for performing 3D vision-language tasks. In our experiments on popular ScanRefer, Multi3DRefer, ScanQA, Sqa3D, and Scan2cap datasets, we demonstrate that our approach outperforms baselines that do not leverage semantic relationships between objects. The code is publicly available at https://github.com/CognitiveAISystems/3DGraphLLM.
FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing
Authors: Bizhu Wu; Jinheng Xie; Meidan Ding; Zhe Kong; Jianfeng Ren; Ruibin Bai; Rong Qu; Linlin Shen
Venue: ICCV 2025 (main)

Abstract: Generating realistic human motions from textual descriptions has undergone significant advancements. However, existing methods often overlook specific body part movements and their timing. In this paper, we address this issue by enriching the textual description with more details. Specifically, we propose the FineMotion dataset, which contains over 442,000 human motion snippets -- short segments of human motion sequences -- and their corresponding detailed descriptions of human body part movements. Additionally, the dataset includes about 95k detailed paragraphs describing the movements of human body parts in entire motion sequences. Experimental results demonstrate the significance of our dataset on the text-driven fine-grained human motion generation task, especially with a remarkable +15.3% improvement in Top-3 accuracy for the MDM model. Notably, we further support a zero-shot pipeline of fine-grained motion editing, which focuses on detailed editing in both spatial and temporal dimensions via text. Dataset and code available at: CVI-SZU/FineMotion
MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs
Authors: Yunqiu Xu; Linchao Zhu; Yi Yang
Venue: ICCV 2025 (main)

Abstract: While multimodal large language models (MLLMs) have demonstrated extraordinary vision-language understanding capabilities, their abilities to solve instance-level visual-language problems beyond a single image warrant further exploration. To assess these unproven abilities of MLLMs, this paper proposes a new visual grounding task called multi-context visual grounding, which aims to localize instances of interest across multiple images based on open-ended text prompts. In order to facilitate this research, we construct a new dataset MC-Bench that features 2K high-quality and manually annotated samples. Each sample consists of an instance-level labeled image pair and a corresponding text prompt that indicates the target instances in the images. These text prompts are highly open-ended and follow three distinct styles, covering 20 practical skills. We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities, along with our developed simple yet effective agentic baseline and a finetuned baseline by multi-context instruction tuning. Our evaluation reveals a non-trivial performance gap between existing MLLMs and humans, along with some insightful observations that suggest potential future directions. We hope that MC-Bench and our empirical findings encourage the research community to further advance the untapped potentials of MLLMs in instance-level tasks, particularly in multi-image contexts. Project page: https://xuyunqiu.github.io/MC-Bench.
What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?
Authors: Jinhong Ni; Chang-Bin Zhang; Qiang Zhang; Jing Zhang
Venue: ICCV 2025 (main)

Abstract: Recent prosperity of text-to-image diffusion models, e.g. Stable Diffusion, has stimulated research to adapt them to 360-degree panorama generation. Prior work has demonstrated the feasibility of using conventional low-rank adaptation techniques on pre-trained diffusion models to generate panoramic images. However, the substantial domain gap between perspective and panoramic images raises questions about the underlying mechanisms enabling this empirical success. We hypothesize and examine that the trainable counterparts exhibit distinct behaviors when fine-tuned on panoramic data, and such an adaptation conceals some intrinsic mechanism to leverage the prior knowledge within the pre-trained diffusion models. Our analysis reveals the following: 1) the query and key matrices in the attention modules are responsible for common information that can be shared between the panoramic and perspective domains, thus are less relevant to panorama generation; and 2) the value and output weight matrices specialize in adapting pre-trained knowledge to the panoramic domain, playing a more critical role during fine-tuning for panorama generation. We empirically verify these insights by introducing a simple framework called UniPano, with the objective of establishing an elegant baseline for future research. UniPano not only outperforms existing methods but also significantly reduces memory usage and training time compared to prior dual-branch approaches, making it scalable for end-to-end panorama generation with higher resolution. The code is available.
CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
Authors: Jiaqi Han; Haotian Ye; Puheng Li; Minkai Xu; James Zou; Stefano Ermon
Venue: ICCV 2025 (main)

Abstract: Diffusion-based generative models have become dominant generators of high-fidelity images and videos but remain limited by their computationally expensive inference procedures. Existing acceleration techniques either require extensive model retraining or compromise significantly on sample quality. This paper explores a general, training-free, and model-agnostic acceleration strategy via multi-core parallelism. Our framework views multi-core diffusion sampling as an ODE solver pipeline, where slower yet accurate solvers progressively rectify faster solvers through a theoretically justified inter-core communication mechanism. This motivates our multi-core training-free diffusion sampling accelerator, CHORDS, which is compatible with various diffusion samplers, model architectures, and modalities. Through extensive experiments, CHORDS significantly accelerates sampling across diverse large-scale image and video diffusion models, yielding up to 2.1x speedup with four cores, improving by 50% over baselines, and 2.9x speedup with eight cores, all without quality degradation. This advancement enables CHORDS to establish a solid foundation for real-time, high-fidelity diffusion generation.
HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics
Authors: Gueter Josmy Faure; Jia-Fong Yeh; Min-Hung Chen; Hung-Ting Su; Shang-Hong Lai; Winston H. Hsu
Venue: ICCV 2025 (main)

Abstract: Long-form video understanding presents unique challenges that extend beyond traditional short-video analysis approaches, particularly in capturing long-range dependencies, processing redundant information efficiently, and extracting high-level semantic concepts. To address these challenges, we propose a novel approach that more accurately reflects human cognition. This paper introduces HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics, featuring two versatile modules that can enhance existing video-language models or operate as a standalone system. Our Episodic COmpressor (ECO) efficiently aggregates representations from micro to semi-macro levels, reducing computational overhead while preserving temporal dependencies. Our Semantics ReTRiever (SeTR) enriches these representations with semantic information by focusing on broader context, dramatically reducing feature dimensionality while preserving relevant macro-level information. We demonstrate that these modules can be seamlessly integrated into existing SOTA models, consistently improving their performance while reducing inference latency by up to 43% and memory usage by 46%. As a standalone system, HERMES achieves state-of-the-art performance across multiple long-video understanding benchmarks in both zero-shot and fully-supervised settings. Our project page and code can be found at https://joslefaure.github.io/assets/html/hermes.html.
CoralSRT: Revisiting Coral Reef Semantic Segmentation by Feature Rectification via Self-supervised Guidance
Authors: Ziqiang Zheng; Yuk-Kwan Wong; Binh-Son Hua; Jianbo Shi; Sai-Kit Yeung
Venue: ICCV 2025 (main)

Abstract: We investigate coral reef semantic segmentation, in which coral reefs are governed by multifaceted factors, like genes, environmental changes, and internal interactions. Unlike segmenting structural units/instances, which are predictable and follow a set pattern, also referred to as commonsense or prior, segmenting coral reefs involves modeling self-repeated, asymmetric, and amorphous distribution of elements, e.g., corals can grow in almost any shape and appearance. We revisited existing segmentation approaches and found that both computer vision and coral reef communities failed to incorporate the intrinsic properties of corals into model design. In this work, we propose a simple formulation for coral reef semantic segmentation: we regard the segment as the basis to model both within-segment and cross-segment affinities. We propose CoralSRT, a feature rectification module via self-supervised guidance, to reduce the stochasticity of coral features extracted by powerful foundation models (FMs), as demonstrated in Fig. 1. We incorporate intrinsic properties of corals to strengthen within-segment affinity by guiding the features within generated segments to align with the centrality. We investigate features from FMs that were optimized by various pretext tasks on significantly large-scale unlabeled or labeled data, which already contain rich information for modeling both within-segment and cross-segment affinities, enabling the adaptation of FMs for coral segmentation. CoralSRT can rectify features from FMs to more efficient features for label propagation and lead to further significant semantic segmentation performance gains, all without requiring additional human supervision, retraining/finetuning FMs or even domain-specific data. These advantages help reduce human effort and the need for domain expertise in data collection and labeling. Our method is easy to implement, and also task- and model-agnostic. CoralSRT bridges the self-supervised pre-training and supervised training in the feature space, also offering insights for segmenting elements/stuffs (e.g., grass, plants, cells, and biofoulings).
VGMamba: Attribute-to-Location Clue Reasoning for Quantity-Agnostic 3D Visual Grounding
Authors: Yihang Zhu; Jinhao Zhang; Yuxuan Wang; Aming Wu; Cheng Deng
Venue: ICCV 2025 (main)

Abstract: As an important direction of embodied intelligence, 3D Visual Grounding has attracted much attention, aiming to identify 3D objects matching the given language description. Most existing methods often follow a two-stage process, i.e., first detecting proposal objects and identifying the right objects based on the relevance to the given query. However, when the query is complex, it is difficult to leverage an abstract language representation to lock the corresponding objects accurately, affecting the grounding performance. In general, given a specific object, humans usually follow two clues to finish the corresponding grounding, i.e., attribute and location clues. To this end, we explore a new mechanism, attribute-to-location clue reasoning, to conduct accurate grounding. Particularly, we propose a VGMamba network that consists of an SVD-based attribute mamba, location mamba, and multi-modal fusion mamba. Taking a 3D point cloud scene and language query as the input, we first exploit SVD to make a decomposition of the extracted features. Then, a slidingwindow operation is conducted to capture attribute characteristics. Next, a location mamba is presented to obtain the corresponding location information. Finally, by means of multi-modal mamba fusion, the model could effectively localize the object that matches the given query. In the experiment, our method is verified on four datasets. Extensive experimental results demonstrate the superiority of our method.
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers For Motion Transfer
Authors: Qingyu Shi; Jianzong Wu; Jinbin Bai; Jiangning Zhang; Lu Qi; Yunhai Tong; Xiangtai Li
Venue: ICCV 2025 (main)

Abstract: The motion transfer task aims to transfer motion from a source video to newly generated videos, requiring the model to decouple motion from appearance. Previous diffusion-based methods primarily rely on separate spatial and temporal attention mechanisms within the 3D U-Net. In contrast, state-of-the-art video Diffusion Transformers (DiT) models use 3D full attention, which does not explicitly separate temporal and spatial information. Thus, the interaction between spatial and temporal dimensions makes decoupling motion and appearance more challenging for DiT models. In this paper, we propose DeT, a method that adapts DiT models to improve motion transfer ability. Our approach introduces a simple yet effective temporal kernel to smooth DiT features along the temporal dimension, facilitating the decoupling of foreground motion from background appearance. Meanwhile, the temporal kernel effectively captures temporal variations in DiT features, which are closely related to motion. Moreover, we introduce explicit supervision along dense trajectories in the latent feature space to further enhance motion consistency. Additionally, we present MTBench, a general and challenging benchmark for motion transfer. We also introduce a hybrid motion fidelity metric that considers both the global and local motion similarity. Therefore, our work provides a more comprehensive evaluation than previous works. Extensive experiments on MTBench demonstrate that DeT achieves the best trade-off between motion fidelity and edit fidelity.
MaskControl: Spatio-Temporal Control for Masked Motion Synthesis
Authors: Ekkasit Pinyoanuntapong; Muhammad Saleem; Korrawe Karunratanakul; Pu Wang; Hongfei Xue; Chen Chen; Chuan Guo; Junli Cao; Jian Ren; Sergey Tulyakov
Venue: ICCV 2025 (main)

Abstract: Recent advances in motion diffusion models have enabled spatially controllable text-to-motion generation. However, these models struggle to achieve high-precision control while maintaining high-quality motion generation. To address these challenges, we propose MaskControl, the first approach to introduce controllability to the generative masked motion model. Our approach introduces two key innovations. First, Logits Regularizer implicitly perturbs logits at training time to align the distribution of motion tokens with the controlled joint positions, while regularizing the categorical token prediction to ensure high-fidelity generation. Second, Logit Optimization explicitly optimizes the predicted logits during inference time, directly reshaping the token distribution that forces the generated motion to accurately align with the controlled joint positions. Moreover, we introduce Differentiable Expectation Sampling (DES) to combat the non-differential distribution sampling process encountered by logits regularizer and optimization. Extensive experiments demonstrate that MaskControl outperforms state-of-the-art methods, achieving superior motion quality (FID decreases by  77%) and higher control precision (average error 0.91 vs. 1.08). Additionally, MaskControl enables diverse applications, including any-joint-any-frame control, body-part timeline control, and zero-shot objective control. Video visualization can be found at https://anonymous-ai-agent.github.io/CAM
Evidential Knowledge Distillation
Authors: Liangyu Xiang; Junyu Gao; Changsheng Xu
Venue: ICCV 2025 (main)

Abstract: Existing logit-based knowledge distillation methods typically employ singularly deterministic categorical distributions, which eliminates the inherent uncertainty in network predictions and thereby limiting the effective transfer of knowledge. To address this limitation, we introduce distribution-based probabilistic modeling as a more comprehensive representation of network knowledge. Specifically, we regard the categorical distribution as a random variable and leverage deep neural networks to predict its distribution, representing it as an evidential second-order distribution. Based on the second-oder modeling, we propose Evidential Knowledge Distillation (EKD) which distills both the expectation of the teacher distribution and the distribution itself into the student. The expectation captures the macroscopic characteristics of the distribution, while the distribution itself conveys microscopic information about the classification boundaries. Additionally, we theoretically show that EKD's distillation objective provides an upper bound on the student's expected risk when treating the teacher's predictions as ground truth. Extensive experiments on several standard benchmarks across various teacher-student network pairs highlight the effectiveness and superior performance of EKD. Our code is available at https://github.com/lyxiang-casia/EKD.
Straighten Viscous Rectified Flow via Noise Optimization
Authors: Jimin Dai; Jiexi Yan; Jian Yang; Lei Luo
Venue: ICCV 2025 (main)

Abstract: The Reflow operation aims to straighten the inference trajectories of the rectified flow during training by constructing deterministic couplings between noises and images, thereby improving the quality of generated images in single-step or few-step generation. However, we identify critical limitations in Reflow, particularly its inability to rapidly generate high-quality images due to a distribution gap between images in its constructed deterministic couplings and real images. To address these shortcomings, we propose a novel alternative called Straighten Viscous Rectified Flow via Noise Optimization (VRFNO), which is a joint training framework integrating an encoder and a neural velocity field. VRFNO introduces two key innovations: (1) a historical velocity term that enhances trajectory distinction, enabling the model to more accurately predict the velocity of the current trajectory, and (2) the noise optimization through reparameterization to form optimized couplings with real images which are then utilized for training, effectively mitigating errors caused by Reflow's limitations. Comprehensive experiments on synthetic data and real datasets with varying resolutions show that VRFNO significantly mitigates the limitations of Reflow, achieving state-of-the-art performance in both one-step and few-step generation tasks.
A Framework for Double-Blind Federated Adaptation of Foundation Models
Authors: Nurbek Tastan; Karthik Nandakumar
Venue: ICCV 2025 (main)

Abstract: Foundation models (FMs) excel in zero-shot tasks but benefit from task-specific adaptation. However, privacy concerns prevent data sharing among multiple data owners, and proprietary restrictions prevent the learning service provider (LSP) from sharing the FM. In this work, we propose BlindFed, a framework enabling collaborative FM adaptation while protecting both parties: data owners do not access the FM or each other's data, and the LSP does not see sensitive task data. BlindFed relies on fully homomorphic encryption (FHE) and consists of three key innovations: (i) FHE-friendly architectural modifications via polynomial approximations and low-rank adapters, (ii) a two-stage split learning approach combining offline knowledge distillation and online encrypted inference for adapter training without backpropagation through the FM, and (iii) a privacy-boosting scheme using sample permutations and stochastic block sampling to mitigate model extraction attacks. Empirical results on four image classification datasets demonstrate the practical feasibility of the BlindFed framework, albeit at a high communication cost and large computational complexity for the LSP. The code can be found at https://github.com/tnurbek/blindfed.
Omni-scene Perception-oriented Point Cloud Geometry Enhancement for Coordinate Quantization
Authors: Wang Liu; Wei Gao
Venue: ICCV 2025 (main)

Abstract: Information quantization has been widely adopted in multimedia content, such as images, videos, and point clouds. The goal of information quantization is to achieve efficient storage and transmission by reducing data precision or redundancy. However, the information distortion caused by quantization will lead to the degradation of signal fidelity and the performance of downstream tasks. This paper focuses on the geometry quantization distortion of point clouds and proposes a unified learning-based quality enhancement framework for omni-scene point clouds. Based on the characteristics of geometry quantization distortion, we analyze and find that existing upsampling methods are not competitive in dealing with point reduction and geometry displacement simultaneously caused by coordinate quantization. Therefore, we design a general rooting-growing-pruning paradigm to efficiently perceive the geometry feature of quantized point clouds and improve the quality significantly. In addition, a novel loss constraint term related to the quantization step parameter is proposed to further improve quality and accelerate model convergence. To the best of our knowledge, this is the first unified quality enhancement framework for object and scene point clouds with coordinate quantization. Extensive experiments verify the superiority of the proposed method on multi-scale point clouds with different levels of quantization distortion, including object (ModelNet40, 8iVFB) and scene (KITTI). In particular, the enhanced point clouds improve the performance of downstream analysis tasks, including classification and 3D object detection.