GenAI4ELab/papercli-papers huggingface dataset:
AV-Flow: Transforming Text to Audio-Visual Human-like Interactions
Authors: Aggelina Chatziagapi; Louis-Philippe Morency; Hongyu Gong; Michael Zollhöfer; Dimitris Samaras; Alexander Richard
Venue: ICCV 2025 (main)

Abstract: We introduce AV-Flow, an audio-visual generative model that animates photo-realistic 4D talking avatars given only text input. In contrast to prior work that assumes an existing speech signal, we synthesize speech and vision jointly. We demonstrate human-like speech synthesis, synchronized lip motion, lively facial expressions and head pose; all generated from just text characters. The core premise of our approach lies in the architecture of our two parallel diffusion transformers. Intermediate highway connections ensure communication between the audio and visual modalities, and thus, synchronized speech intonation and facial dynamics (e.g., eyebrow motion). Our model is trained with flow matching, leading to expressive results and fast inference. In case of dyadic conversations, AV-Flow produces an always-on avatar, that actively listens and reacts to the audio-visual input of a user. Through extensive experiments, we show that our method outperforms prior work, synthesizing natural-looking 4D talking avatars.
Multi-View Slot Attention Using Paraphrased Texts for Face Anti-Spoofing
Authors: Jeongmin Yu; Susang Kim; Kisu Lee; Taekyoung Kwon; Won-Yong Shin; Ha Young Kim
Venue: ICCV 2025 (main)

Abstract: Recent face anti-spoofing (FAS) methods have shown remarkable cross-domain performance by employing vision-language models like CLIP. However, existing CLIP-based FAS models do not fully exploit CLIP's patch embedding tokens, failing to detect critical spoofing clues. Moreover, these models rely on a single text prompt per class (e.g, 'live' or 'fake'), which limits generalization. To address these issues, we propose MVP-FAS, a novel framework incorporating two key modules: Multi-View Slot attention (MVS) and Multi-Text Patch Alignment (MTPA). Both modules utilize multiple paraphrased texts to generate generalized features and reduce dependence on domain-specific text. MVS extracts local detailed spatial features and global context from patch embeddings by leveraging diverse texts with multiple perspectives. MTPA aligns patches with multiple text representations to improve semantic robustness. Extensive experiments demonstrate that MVP-FAS achieves superior generalization performance, outperforming previous state-of-the-art methods on cross-domain datasets.
Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos
Authors: Chengbo Yuan; Geng Chen; Li Yi; Yang Gao
Venue: ICCV 2025 (main)

Abstract: Egocentric videos provide valuable insights into human interactions with the physical world, which has sparked growing interest in the computer vision and robotics communities. A critical challenge in fully understanding the geometry and dynamics of egocentric videos is dense scene reconstruction. However, the lack of high-quality labeled datasets in this field has hindered the effectiveness of current supervised learning methods. In this work, we aim to address this issue by exploring an self-supervised dynamic scene reconstruction approach. We introduce **EgoMono4D**, a novel model that unifies the estimation of multiple variables necessary for *Ego*centric *Mono*cular *4D* reconstruction, including camera intrinsic, camera poses, and video depth, all within a fast feed-forward framework. Starting from pretrained single-frame depth and intrinsic estimation model, we extend it with camera poses estimation and align multi-frame results on large-scale unlabeled egocentric videos. We evaluate EgoMono4D in both in-domain and zero-shot generalization settings, achieving superior performance in dense pointclouds sequence reconstruction compared to all baselines. EgoMono4D represents the first attempt to apply self-supervised learning for pointclouds sequence reconstruction to the label-scarce egocentric field, enabling fast, dense, and generalizable reconstruction. The interactable visualization, code and trained models are released https://egomono4d.github.io/.
What we need is explicit controllability: Training 3D gaze estimator using only facial images
Authors: Tingwei Li; Jun Bao; Zhenzhong Kuang; Buyu Liu
Venue: ICCV 2025 (main)

Abstract: This work focuses on unsupervised 3D gaze estimation. Specifically, we adopt a learning-by-synthesis approach that trains a gaze prediction model using simulated data. Unlike existing methods that lack explicit and accurate control over facial images--particularly the eye regions--we propose a geometrically meaningful 3D representation that enables diverse, precise, and explicit control over illumination, eye regions, and gaze targets using only facial images. Given a sequence of facial images, our method constructs a mesh representation where each mesh is associated with 3D Gaussians, enabling explicit lighting control. To further enhance realism, we introduce eye-focused constraints, including a rotation symmetry protocol, as well as geometry and appearance losses for the eye regions, alongside conventional learning objectives. Additionally, we incorporate a virtual screen target and rotate the eyeballs accordingly, generating more accurate pseudo gaze directions paired with realistic facial images. We validate our approach through extensive experiments on three benchmarks. The results demonstrate that our trained gaze estimators outperform all unsupervised baselines and achieve performance comparable to cross-dataset approaches. Furthermore, our method generates the most visually realistic images, as confirmed by both objective and subjective image quality metrics. Our code is available at https://github.com/ATinyBites/ControllableGaze.
WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection
Authors: Haodong Zhu; Wenhao Dong; Linlin Yang; Hong Li; Yuguang Yang; Yangyang Ren; Qingcheng Zhu; Zichao Feng; Changbai Li; Shaohui Lin; Runqi Wang; Xiaoyan Luo; Baochang Zhang
Venue: ICCV 2025 (main)

Abstract: Leveraging the complementary characteristics of visible (RGB) and infrared (IR) imagery offers significant potential for improving object detection. In this paper, we propose WaveMamba, a cross-modality fusion method that efficiently integrates the unique and complementary frequency features of RGB and IR decomposed by Discrete Wavelet Transform (DWT). An improved detection head incorporating the Inverse Discrete Wavelet Transform (IDWT) is also proposed to reduce information loss and produce the final detection results. The core of our approach is the introduction of WaveMamba Fusion Block (WMFB), which facilitates comprehensive fusion across low-/high-frequency sub-bands. Within WMFB, the Low-frequency Mamba Fusion Block (LMFB), built upon the Mamba framework, first performs initial low-frequency feature fusion with channel swapping, followed by deep fusion with an advanced gated attention mechanism for enhanced integration. High-frequency features are enhanced using a strategy that applies an "absolute maximum" fusion approach. These advancements lead to significant performance gains, with our method surpassing state-of-the-art approaches and achieving average mAP improvements of 4.5% on four benchmarks.
CoST: Efficient Collaborative Perception From Unified Spatiotemporal Perspective
Authors: Zongheng Tang; Yi Liu; Yifan Sun; Yulu Gao; Jinyu Chen; Runsheng Xu; Si Liu
Venue: ICCV 2025 (main)

Abstract: Collaborative perception shares information among different agents and helps solving problems that individual agents may face, e.g., occlusions and small sensing range. Prior methods usually separate the multi-agent fusion and multi-time fusion into two consecutive steps. In contrast, this paper proposes an efficient collaborative perception that aggregates the observations from different agents (space) and different times into a unified spatio-temporal space simultanesouly. The unified spatio-temporal space brings two benefits, i.e., efficient feature transmission and superior feature fusion. 1) Efficient feature transmission: each static object yields a single observation in the spatial temporal space, and thus only requires transmission only once (whereas prior methods re-transmit all the object features multiple times). 2) superior feature fusion: merging the multi-agent and multi-time fusion into a unified spatial-temporal aggregation enables a more holistic perspective, thereby enhancing perception performance in challenging scenarios. Consequently, our Collaborative perception with Spatio-temporal Transformer (CoST) gains improvement in both efficiency and accuracy. Notably, CoST is not tied to any specific method and is compatible with a majority of previous methods, enhancing their accuracy while reducing the transmission bandwidth.
GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion
Authors: Li-Heng Chen; Zi-Xin Zou; Chang Liu; Tianjiao Jing; Yan-Pei Cao; Shi-Sheng Huang; Hongbo Fu; Hua Huang
Venue: ICCV 2025 (main)

Abstract: Accurate surface reconstruction from unposed images is crucial for efficient 3D object or scene creation. However, it remains challenging, particularly for the joint camera pose estimation. Previous approaches have achieved impressive pose-free surface reconstruction results in dense-view settings, but could easily fail for sparse-view scenarios without sufficient visual overlap. In this paper, we propose a new technique for pose-free surface reconstruction, which follows triplane-based signed distance field (SDF) learning but regularizes the learning by explicit points sampled from ray-based diffusion of camera pose estimation. Our key contribution is a novel Geometric Consistent Ray Diffusion model (GCRayDiffusion), where we represent camera poses as neural bundle rays and regress the distribution of noisy rays via a diffusion model. More importantly, we further condition the denoising process of RGRayDiffusion using the triplane-based SDF of the entire scene, which provides effective 3D consistent regularization to achieve multi-view consistent camera pose estimation. Finally, we incorporate RGRayDiffusion into the triplane-based SDF learning by introducing on-surface geometric regularization from the sampling points of the neural bundle rays, which leads to highly accurate pose-free surface reconstruction results even for sparse-view inputs. Extensive evaluations on public datasets show that our GCRayDiffusion achieves more accurate camera pose estimation than previous approaches, with geometrically more consistent surface reconstruction results, especially given sparse-view inputs.
AU-Blendshape for Fine-grained Stylized 3D Facial Expression Manipulation
Authors: Hao Li; Ju Dai; Feng Zhou; Kaida  Ning; Lei Li; Junjun Pan
Venue: ICCV 2025 (main)

Abstract: While 3D facial animation has made impressive progress, challenges still exist in realizing fine-grained stylized 3D facial expression manipulation due to the lack of appropriate datasets. In this paper, we introduce the AUBlendSet, a 3D facial dataset based on AU-Blendshape representation for fine-grained facial expression manipulation across identities. AUBlendSet is a blendshape data collection based on 32 standard facial action units (AUs) across 500 identities, along with an additional set of facial postures annotated with detailed AUs. Based on AUBlendSet, we propose AUBlendNet to learn AU-Blendshape basis vectors for different character styles. AUBlendNet predicts, in parallel, the AU-Blendshape basis vectors of the corresponding style for a given identity mesh, thereby achieving stylized 3D emotional facial manipulation. We comprehensively validate the effectiveness of AUBlendSet and AUBlendNet through tasks such as stylized facial expression manipulation, speech-driven emotional facial animation, and emotion recognition data augmentation. Through a series of qualitative and quantitative experiments, we demonstrate the potential and importance of AUBlendSet and AUBlendNet in 3D facial animation tasks. To the best of our knowledge, AUBlendSet is the first dataset, and AUBlendNet is the first network for continuous 3D facial expression manipulation for any identity through facial AUs. Our source code is available at https://github.com/wslh852/AUBlendNet.git.
Noise-Modeled Diffusion Models for Low-Light Spike Image Restoration
Authors: Ruonan Liu; Lin Zhu; Xijie Xiang; Lizhi Wang; Hua Huang
Venue: ICCV 2025 (main)

Abstract: Spike-based imaging, inspired by the human visual system, offers several advantages, including high temporal resolution and low power consumption, but suffers from significant image degradation in low-light conditions due to noise interference. Restoring spike images under such conditions poses a significant challenge, as traditional frame-based or spike-based techniques are ill-suited to handle such severe noise and unique noise characteristics. This paper proposes a novel approach for restoring low-light spike images using noise-modeled diffusion models. By establishing a noise-embedded spike imaging model under low light, we model the forward diffusion process as the degradation of spike images with proportional and residual terms and incorporate deterministic and non-deterministic components with reverse shifting, enabling the model to capture the distinctive spike noise structure. Additionally, we utilize region mask image, dark current map and spike density value as conditions to further guide the restoration process by providing prompts for degradation regions, deterministic parameters and noise intensity, respectively. Experimental results demonstrate that our method significantly outperforms existing spike-based reconstruction and diffusion-based image restoration methods in both quantitative performance and visual quality. The code and dataset are available at https://github.com/BIT-Vision/SpikeDiffusion.
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis
Authors: Peng Zheng; Junke Wang; Yi Chang; Yizhou Yu; Rui Ma; Zuxuan Wu
Venue: ICCV 2025 (main)

Abstract: Recent advances in large language models (LLMs) have spurred interests in encoding images as discrete tokens and leveraging autoregressive (AR) frameworks for visual generation. However, the quantization process in AR-based visual generation models inherently introduces information loss that degrades image fidelity. To mitigate this limitation, recent studies have explored to autoregressively predict continuous tokens. Unlike discrete tokens that reside in a structured and bounded space, continuous representations exist in an unbounded, high-dimensional space, making density estimation more challenging and increasing the risk of generating out-of-distribution artifacts. Based on the above findings, this work introduces DisCon (Discrete-Conditioned Continuous Autoregressive Model), a novel framework that reinterprets discrete tokens as conditional signals rather than generation targets. By modeling the conditional probability of continuous representations conditioned on discrete tokens, DisCon circumvents the optimization challenges of continuous token modeling while avoiding the information loss caused by quantization. DisCon achieves a gFID score of 1.38 on ImageNet 256x256 generation, outperforming state-of-the-art autoregressive approaches by a clear margin. Project page: https://pengzheng0707.github.io/DisCon.
MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm
Authors: Ziyan Guo; Zeyu Hu; De Wen Soh; Na Zhao
Venue: ICCV 2025 (main)

Abstract: Human motion generation and editing are key components of computer vision. However, current approaches in this field tend to offer isolated solutions tailored to specific tasks, which can be inefficient and impractical for real-world applications. While some efforts have aimed to unify motion-related tasks, these methods simply use different modalities as conditions to guide motion generation. Consequently, they lack editing capabilities, fine-grained control, and fail to facilitate knowledge sharing across tasks. To address these limitations and provide a versatile, unified framework capable of handling both human motion generation and editing, we introduce a novel paradigm: Motion-Condition-Motion, which enables the unified formulation of diverse tasks with three concepts: source motion, condition, and target motion. Based on this paradigm, we propose a unified framework, MotionLab, which incorporates rectified flows to learn the mapping from source motion to target motion, guided by the specified conditions. In MotionLab, we introduce the 1) MotionFlow Transformer to enhance conditional generation and editing without task-specific modules; 2) Aligned Rotational Position Encoding to guarantee the time synchronization between source motion and target motion; 3) Task Specified Instruction Modulation; and 4) Motion Curriculum Learning for effective multi-task learning and knowledge sharing across tasks. Notably, our MotionLab demonstrates promising generalization capabilities and inference efficiency across multiple benchmarks for human motion. Our code and additional video results are available at: https://diouo.github.io/motionlab.github.io/.
Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis
Authors: Byung Hyun Lee; Wongi Jeong; Woojae Han; Kyoungbun Lee; Se Young Chun
Venue: ICCV 2025 (main)

Abstract: Multiple instance learning (MIL) significantly reduced annotation costs via bag-level weak labels for large-scale images, such as histopathological whole slide images (WSIs). However, its adaptability to continual tasks with minimal forgetting has been rarely explored, especially on instance classification for localization. Weakly incremental learning for semantic segmentation has been studied for continual localization, but it focused on natural images, leveraging global relationships among hundreds of small patches (e.g., 16 x 16) using pre-trained models. This approach seems infeasible for MIL localization due to enormous amounts (up to 100,000) of large patches (e.g., 256 x 256) and no available global relationships such as cancer cells. To address these challenges, we propose Continual Multiple Instance Learning with Enhanced Localization (CoMEL), an MIL framework for both localization and adaptability with minimal forgetting. CoMEL consists of (1) Grouped Double Attention Transformer (GDAT) for efficient instance encoding, (2) Bag Prototypes-based Pseudo-Labeling (BPPL) for reliable instance pseudo-labeling, and (3) Orthogonal Weighted Low-Rank Adaptation (OWLoRA) to mitigate forgetting in both bag and instance classification. Extensive experiments on three public WSI datasets demonstrate superior performance of CoMEL, outperforming the prior arts by up to 11.00% in bag-level accuracy and up to 23.4% in localization accuracy under the continual MIL setup.
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding
Authors: Jungbin Cho; Junwan Kim; Jisoo Kim; Minseo Kim; Mingu Kang; Sungeun Hong; Tae-Hyun Oh; Youngjae Yu
Venue: ICCV 2025 (main)

Abstract: Human motion is inherently continuous and dynamic, posing significant challenges for generative models. While discrete generation methods are widely used, they suffer from limited expressiveness and frame-wise noise artifacts. In contrast, continuous approaches produce smoother, more natural motion but often struggle to adhere to conditioning signals due to high-dimensional complexity and limited training data. To resolve this discord between discrete and continuous representations we introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding, a novel method that leverages rectified flow to decode discrete motion tokens in the continuous, raw motion space. Our core idea is to frame token decoding as a conditional generation task, ensuring that DisCoRD captures fine-grained dynamics and achieves smoother, more natural motions. Compatible with any discrete-based framework, our method enhances naturalness without compromising faithfulness to the conditioning signals on diverse settings. Extensive evaluations demonstrate that DisCoRD achieves state-of-the-art performance, with FID of 0.032 on HumanML3D and 0.169 on KIT-ML. These results establish DisCoRD as a robust solution for bridging the divide between discrete efficiency and continuous realism. Code and checkpoints will be released.
TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training
Authors: Felix Krause; Timy Phan; Ming Gui; Stefan Andreas Baumann; Vincent Tao Hu; Björn Ommer
Venue: ICCV 2025 (main)

Abstract: Diffusion models have emerged as the mainstream approach for visual generation. However, these models typically suffer from sample inefficiency and high training costs. Consequently, methods for efficient finetuning, inference and personalization were quickly adopted by the community. However, training these models in the first place still remains very costly. While several recent approaches--including masking, distillation, and architectural modifications--have been proposed to improve training efficiency, each of these methods comes with its own tradeoffs: some achieve enhanced performance at the expense of increased computational cost. In contrast, this work aims to improve training efficiency as well as generative performance at the same time through routes that act as transport mechanism for randomly selected tokens from early layers to deeper layers of the model. Our method is not limited to the common transformer-based model - it can also be applied to state-space models and achieves this without architectural modifications or additional parameters. Finally, we show that TREAD reduces the computational cost and simultaneously boosts model performance on the standard benchmark ImageNet-256 in class-conditional synthesis. Both of these benefits multiply to a convergence speedup of 14x at 400K training iterations compared to DiT and 37x compared to the best benchmark performance of DiT at 7M training iterations. Further, we achieve a competitive FID of 2.09 in a guided and 3.93 in an unguided setting which improves upon the DiT, without architectural changes. We will release our code.
Leveraging Debiased Cross-modal Attention Maps and Code-based Reasoning for Zero-shot Referring Expression Comprehension
Authors: Juntao Chen; Wen Shen; Zhihua Wei; Lijun Sun; Hongyun Zhang
Venue: ICCV 2025 (main)

Abstract: Zero-shot Referring Expression Comprehension (REC) aims at locating an object described by a natural language query without training on task-specific datasets. Current approaches often utilize Vision-Language Models (VLMs) to perform region-text matching based on region proposals. However, this may downgrade their performance since VLMs often fail in relation understanding and isolated proposals inevitably lack global image context. To tackle these challenges, we first design a general formulation for code-based relation reasoning. It instructs Large Language Models (LLMs) to decompose complex relations and adaptively implement code for spatial and relation computation. Moreover, we directly extract region-text relevance from cross-modal attention maps in VLMs. Observing the inherent bias in VLMs, we further develop a simple yet effective bias deduction method, which enhances attention maps' capability to align text with the corresponding regions. Experimental results on four representative datasets demonstrate the SOTA performance of our method. On the RefCOCO dataset centered on spatial understanding, our method gets an average improvement of 10% over the previous zero-shot SOTA.