GenAI4ELab/papercli-papers huggingface dataset:
Disentangled Clothed Avatar Generation from Text Descriptions
Authors: Jionghao Wang; Yuan Liu; Zhiyang Dou; Zhengming Yu; Yongqing Liang; Cheng Lin; Rong Xie; Li Song; Xin Li; Wenping Wang
Venue: ECCV 2024 (main)

Abstract: "In this paper, we introduce a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated avatar. While recent advancements in text-to-avatar generation have yielded diverse human avatars from text prompts, these methods typically combine all elements—clothes, hair, and body—into a single 3D representation. Such an entangled approach poses challenges for downstream tasks like editing or animation. To overcome these limitations, we propose a novel disentangled 3D avatar representation named Sequentially Offset-SMPL (SO-SMPL), building upon the SMPL model. SO-SMPL represents the human body and clothes with two separate meshes but associates them with offsets to ensure the physical alignment between the body and the clothes. Then, we design a Score Distillation Sampling (SDS)-based distillation framework to generate the proposed SO-SMPL representation from text prompts. Our approach not only achieves higher texture and geometry quality and better semantic alignment with text prompts, but also significantly improves the visual quality of character animation, virtual try-on, and avatar editing. Project page: this link."
Real Appearance Modeling for More General Deepfake Detection
Authors: Jiahe Tian; Cai Yu; Xi Wang; Peng Chen; Zihao Xiao; Jiao Dai; Yesheng Chai; Jizhong Han
Venue: ECCV 2024 (main)

Abstract: "Recent studies in deepfake detection have shown promising results when detecting deepfakes of the same type as those present in training. However, their ability to generalize to unseen deepfakes remains limited. This work improves the generalizable deepfake detection from a simple principle: an ideal detector classifies any face that contains anomalies not found in real faces as fake. Namely, detectors should learn consistent real appearances rather than fake patterns in the training set that may not apply to unseen deepfakes. Guided by this principle, we propose a learning task named Real Appearance Modeling (RAM) that guides the model to learn real appearances by recovering original faces from slightly disturbed faces. We further propose Face Disturbance to produce disturbed faces while preserving original information that enables recovery, which aids the model in learning the fine-grained appearance of real faces. Extensive experiments demonstrate the effectiveness of modeling real appearances to spot richer deepfakes. Our method surpasses existing state-of-the-art methods by a large margin on multiple popular deepfake datasets."
6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model
Authors: Matteo Bortolon; Theodore Tsesmelis; Stuart James; Fabio Poiesi; Alessio Del Bue
Venue: ECCV 2024 (main)

Abstract: "We propose to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. avoids the iterative process typical of analysis-by-synthesis methods (iNeRF) that also require an initialization of the camera pose in order to converge. Instead, our method estimates a 6DoF pose by inverting the 3DGS rendering process. Starting from the object surface, we define a radiant that uniformly generates rays departing from each ellipsoid that parameterize the 3DGS model. Each ray is associated with the rendering parameters of each ellipsoid, which in turn is used to obtain the best bindings between the target image pixels and the cast rays. These pixel-ray bindings are then ranked to select the best scoring bundle of rays, which their intersection provides the camera center and, in turn, the camera rotation. The proposed solution obviates the necessity of an “a priori” pose for initialization, and it solves 6DoF pose estimation in closed form, without the need for iterations. Moreover, compared to the existing Novel View Synthesis (NVS) baselines for pose estimation, can improve the overall average rotational accuracy by 12% and translation accuracy by 22% on real scenes, despite not requiring any initialization pose. At the same time, our method operates near real-time, reaching 15f ps on consumer hardware. Project page: https://mbortolon97.github.io/6dgs/ Corresponding author: mbortolon@fbk.eu"
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Authors: Jia-Hao Xiao; Ming-Kun Xie; Heng-Bo Fan; Gang Niu; Masashi Sugiyama; Sheng-Jun Huang
Venue: ECCV 2024 (main)

Abstract: "Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. To solve this problem, the mainstream method developed an effective thresholding strategy to generate accurate pseudo-labels. Unfortunately, the method neglected the quality of model predictions and its potential impact on pseudo-labeling performance. In this paper, we propose a dual-perspective method to generate high-quality pseudo-labels. To improve the quality of model predictions, we perform dual-decoupling to boost the learning of correlative and discriminative features, while refining the generation and utilization of pseudo-labels. To obtain proper class-wise thresholds, we propose the metric-adaptive thresholding strategy to estimate the thresholds, which maximize the pseudo-label performance for a given metric on labeled data. Experiments on multiple benchmark datasets show the proposed method can achieve the state-of-the-art performance and outperform the comparative methods with a significant margin. The implementation is available at JiahaoXxX/SSMLL-D2L MAT."
V2X-Real: a Largs-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Authors: Hao Xiang; Xin Xia; Zhaoliang Zheng; Runsheng Xu; Letian Gao; Zewei Zhou; xu han; Xinkai Ji; Mingxi Li; Zonglin Meng; Li Jin; Mingyue Lei; Zhaoyang Ma; Zihang He; Haoxuan Ma; Yunshuang Yuan; Yingqian Zhao; Jiaqi Ma
Venue: ECCV 2024 (main)

Abstract: "Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets to facilitate the real V2X cooperative perception research – existing datasets either only support Vehicle-to-Infrastructure cooperation or Vehicle-to-Vehicle cooperation. In this paper, we present V2X-Real, a large-scale dataset that includes a mixture of multiple vehicles and smart infrastructure to facilitate the V2X cooperative perception development with multi-modality sensing data. Our V2X-Real is collected using two connected automated vehicles and two smart infrastructure, which are all equipped with multi-modal sensors including LiDAR sensors and multi-view cameras. The whole dataset contains 33K LiDAR frames and 171K camera data with over 1.2M annotated bounding boxes of 10 categories in very challenging urban scenarios. According to the collaboration mode and ego perspective, we derive four types of datasets for Vehicle-Centric, Infrastructure-Centric, Vehicle-to-Vehicle, and Infrastructure-to-Infrastructure cooperative perception. Comprehensive multi-class multi-agent benchmarks of SOTA cooperative perception methods are provided. The V2X-Real dataset and codebase are available at https://mobility-lab.seas.ucla.edu/v2x-real."
VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space
Authors: Guénolé Fiche; Simon Leglaive; Xavier Alameda-Pineda; Antonio Agudo; Francesc Moreno
Venue: ECCV 2024 (main)

Abstract: "Previous works on Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techniques leverage a low-dimensional statistical body model for realistic results, whereas recent non-parametric methods achieve higher precision by directly regressing the 3D coordinates of the human body mesh. This work introduces a novel paradigm to address the HPSE problem, involving a low-dimensional discrete latent representation of the human mesh and framing HPSE as a classification task. Instead of predicting body model parameters or 3D vertex coordinates, we focus on predicting the proposed discrete latent representation, which can be decoded into a registered human mesh. This innovative paradigm offers two key advantages. Firstly, predicting a low-dimensional discrete representation confines our predictions to the space of anthropomorphic poses and shapes even when little training data is available. Secondly, by framing the problem as a classification task, we can harness the discriminative power inherent in neural networks. The proposed model, VQ-HPS, predicts the discrete latent representation of the mesh. The experimental results demonstrate that VQ-HPS outperforms the current state-of-the-art non-parametric approaches while yielding results as realistic as those produced by parametric methods when trained with few data. VQ-HPS also shows promising results when training on large-scale datasets, highlighting the significant potential of the classification approach for HPSE. See the project page at https://g-fiche.github.io/research-pages/vqhps/."
Attention Beats Linear for Fast Implicit Neural Representation Generation
Authors: Shuyi Zhang; Ke Liu; Jingjun Gu; Xiaoxu Cai; Zhihua Wang; Jiajun Bu; Haishuai Wang
Venue: ECCV 2024 (main)

Abstract: "Implicit Neural Representation (INR) has gained increasing popularity as a data representation method, serving as a prerequisite for innovative generation models. Unlike gradient-based methods, which exhibit lower efficiency in inference, the adoption of hyper-network for generating parameters in Multi-Layer Perceptrons (MLP), responsible for executing INR functions, has surfaced as a promising and efficient alternative. However, as a global continuous function, MLP is challenging in modeling highly discontinuous signals, resulting in slow convergence during the training phase and inaccurate reconstruction performance. Moreover, MLP requires massive representation parameters, which implies inefficiencies in data representation. In this paper, we propose a novel Attention-based Localized INR (ANR) composed of a localized attention layer (LAL) and a global MLP that integrates coordinate features with data features and converts them to meaningful outputs. Subsequently, we design an instance representation framework that delivers a transformer-like hyper-network to represent data instances as a compact representation vector. With instance-specific representation vector and instance-agnostic ANR parameters, the target signals are well reconstructed as a continuous function. We further address aliasing artifacts with variational coordinates when obtaining the super-resolution inference results. Extensive experimentation across four datasets showcases the notable efficacy of our ANR method, e.g. enhancing the PSNR value from 37.95dB to 47.25dB on the CelebA dataset. Code is released at https://github.com/Roninton/ANR."
HARIVO: Harnessing Text-to-Image Models for Video Generation
Authors: Mingi Kwon; Seoung Wug Oh; Yang Zhou; Joon-Young Lee; Difan Liu; Haoran Cai; Baqiao Liu; Feng Liu; Youngjung Uh
Venue: ECCV 2024 (main)

Abstract: "We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Recently, AnimateDiff proposed freezing the T2I model while only training temporal layers. We advance this method by proposing a unique architecture, incorporating a mapping network and frame-wise tokens, tailored for video generation while maintaining the diversity and creativity of the original T2I model. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique, ensuring realistic and temporally consistent video generation despite limited public video data. We have successfully integrated video-specific inductive biases into the architecture and loss functions. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth. project page: https://kwonminki.github.io/HARIVO/"
Deep Online Probability Aggregation Clustering
Authors: Yuxuan Yan; Na Lu; Ruofan Yan
Venue: ECCV 2024 (main)

Abstract: "Combining machine clustering with deep models has shown remarkable superiority in deep clustering. It modifies the data processing pipeline into two alternating phases: feature clustering and model training. However, such alternating schedules may lead to instability and computational burden issues. To tackle these problems, we propose a centerless clustering algorithm called Probability Aggregation Clustering (PAC), enabling easy deployment in online deep clustering. PAC circumvents the cluster center and aligns the probability space and distribution space by formulating clustering as an optimization problem with a novel objective function. Based on the computation mechanism of the PAC, we propose a general online probability aggregation module to perform stable and flexible feature clustering over mini-batch data and further construct a deep visual clustering framework deep PAC (DPAC). Extensive experiments demonstrate that DPAC remarkably outperforms the state-of-the-art deep clustering methods.1 1 The code is available at Deep-Probability-Aggregation-Clustering https://github.com/aomandechenai/"
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification
Authors: Yonggan Wu; Ling-Chao Meng; Yuan Zichao; Sixian Chan; Hong-Qiang Wang
Venue: ECCV 2024 (main)

Abstract: "For the visible-infrared person re-identification (VI-ReID) task, one of the primary challenges lies in significant cross-modality discrepancy. Existing methods struggle to conduct modality-invariant information mining. They often focus solely on mining singular dimensions like spatial or channel, and overlook the extraction of specific-modality multi-dimension information. To fully mine modality-invariant information across a wide range, we introduce the Wide-Ranging Information Mining Network (WRIM-Net), which mainly comprises a Multi-dimension Interactive Information Mining (MIIM) module and an Auxiliary-Information-based Contrastive Learning (AICL) approach. Empowered by the proposed Global Region Interaction (GRI), MIIM comprehensively mines non-local spatial and channel information through intra-dimension interaction. Moreover, Thanks to the low computational complexity design, separate MIIM can be positioned in shallow layers, enabling the network to better mine specific-modality multi-dimension information. AICL, by introducing the novel Cross-Modality Key-Instance Contrastive (CMKIC) loss, effectively guides the network in extracting modality-invariant information. We conduct extensive experiments not only on the well-known SYSU-MM01 and RegDB datasets but also on the latest large-scale cross-modality LLCM dataset. The results demonstrate WRIM-Net’s superiority over state-of-the-art methods."
Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models
Authors: Chao Gong; Kai Chen; Zhipeng Wei; Jingjing Chen; Yu-Gang Jiang
Venue: ECCV 2024 (main)

Abstract: "Text-to-image models encounter safety issues, including concerns related to copyright and Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for erasing inappropriate concepts from diffusion models, they often exhibit incomplete erasure, consume a lot of computing resources, and inadvertently damage generation ability. In this work, we introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model in 3 seconds without necessitating additional fine-tuning. Specifically, RECE efficiently leverages a closed-form solution to derive new target embeddings, which are capable of regenerating erased concepts within the unlearned model. To mitigate inappropriate content potentially represented by derived embeddings, RECE further aligns them with harmless concepts in cross-attention layers. The derivation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts. Besides, to preserve the model’s generation ability, RECE introduces an additional regularization term during the derivation process, resulting in minimizing the impact on unrelated concepts during the erasure process. All the processes above are in closed-form, guaranteeing extremely efficient erasure in only 3 seconds. Benchmarking against previous approaches, our method achieves more efficient and thorough erasure with minor damage to original generation ability and demonstrates enhanced robustness against red-teaming tools. Code is available at https://github. com/CharlesGong12/RECE. redWARNING: This paper contains model outputs that may be offensive."
Visual Text Generation in the Wild
Authors: Yuanzhi Zhu; Jiawei Liu; Feiyu Gao; Wenyu Liu; Xinggang Wang; Peng Wang; Fei Huang; Cong Yao; Zhibo Yang
Venue: ECCV 2024 (main)

Abstract: "Recently, with the rapid advancements of generative models, the field of visual text generation has witnessed significant progress. However, it is still challenging to render high-quality text images in real-world scenarios, as three critical criteria should be satisfied: (1) Fidelity: the generated text images should be photo-realistic and the contents are expected to be the same as specified in the given conditions; (2) Reasonability: the regions and contents of the generated text should cohere with the scene; (3) Utility: the generated text images can facilitate related tasks (, text detection and recognition). Upon investigation, we find that existing methods, either rendering-based or diffusion-based, can hardly meet all these aspects simultaneously, limiting their application range. Therefore, we propose in this paper a visual text generator (termed SceneVTG), which can produce high-quality text images in the wild. Following a two-stage paradigm, SceneVTG leverages a Multimodal Large Language Model to recommend reasonable text regions and contents across multiple scales and levels, which are used by a conditional diffusion model as conditions to generate text images. Extensive experiments demonstrate that the proposed SceneVTG significantly outperforms traditional rendering-based methods and recent diffusion-based methods in terms of fidelity and reasonability. Besides, the generated images provide superior utility for tasks involving text detection and text recognition. Code and datasets are available at magentaAdvancedLiterateMachinery."
Length-Aware Motion Synthesis via Latent Diffusion
Authors: Alessio Sampieri; Alessio Palma; Indro Spinelli; Fabio Galasso
Venue: ECCV 2024 (main)

Abstract: "The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is not merely fast-forwarding it. However, state-of-the-art techniques for human behavior synthesis have limited control over the target sequence length. We introduce the problem of generating length-aware 3D human motion sequences from textual descriptors, and we propose a novel model to synthesize motions of variable target lengths, which we dub “Length-Aware Latent Diffusion” (LADiff ). LADiff consists of two new modules: 1) a length-aware variational auto-encoder to learn motion representations with length-dependent latent codes; 2) a length-conforming latent diffusion model to generate motions with a richness of details that increases with the required target sequence length. LADiff significantly improves over the state-of-the-art across most of the existing motion synthesis metrics on the two established benchmarks of HumanML3D and KIT-ML. The code is available at https://github.com/AlessioSam/LADiff."
Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
Authors: Yunlong Zhang; Honglin Li; YUXUAN SUN; Chenglu Zhu; Sunyi Zheng; Lin Yang
Venue: ECCV 2024 (main)

Abstract: "In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting. To mitigate overfitting, we present Attention-Challenging MIL (ACMIL). ACMIL combines two techniques based on separate analyses for attention value concentration. Firstly, UMAP of instance features reveals various patterns among discriminative instances, with existing attention mechanisms capturing only some of them. To remedy this, we introduce Multiple Branch Attention (MBA) to capture more discriminative instances using multiple attention branches. Secondly, the examination of the cumulative value of Top-K attention scores indicates that a tiny number of instances dominate the majority of attention. In response, we present Stochastic Top-K Instance Masking (STKIM), which masks out a portion of instances with Top-K attention values and allocates their attention values to the remaining instances. The extensive experimental results on three WSI datasets with two pre-trained backbones reveal that our ACMIL outperforms state-of-the-art methods. Additionally, through heatmap visualization and UMAP visualization, this paper extensively illustrates ACMIL’s effectiveness in suppressing attention value concentration and overcoming the overfitting challenge. The source code is available at https://github.com/dazhangyu123/ACMIL."
An Optimal Control View of LoRA and Binary Controller Design for Vision Transformers
Authors: Chi Zhang; Jingpu Cheng; Qianxiao Li
Venue: ECCV 2024 (main)

Abstract: "While recent advancements in model fine-tuning predominantly emphasize the utilization of low-rank adaptation (LoRA), we propose an alternative approach centered on reducing the precision of adaptation matrices. In particular, we depart from the common viewpoint that considers adaptation matrices solely as weight differences, and reinterpret them as “control variables” to perturb pre-trained ViT systems. This new perspective enables the establishment of a control-oriented framework, facilitating the exploration of optimal controls guided by the Pontryagin Maximum Principle. Furthermore, we demonstrate that for bounded control sets such as hypercubes, the optimal controls often take on boundary values, leading naturally to a binary controller design. Theoretical analysis reveals that employing a binary control strategy achieves the same reachable state as its full-precision counterpart in the continuous idealisation of deep residual structures, a finding corroborated by later empirical investigations. Our studies further indicate that the controller’s rank holds greater significance than its precision. As such, opting for low-precision yet high-rank controls is demonstrated to obtain better performance for practical vision tasks."