GenAI4ELab/papercli-papers huggingface dataset:
Diffusion Model is a Good Pose Estimator from 3D RF-Vision
Authors: Junqiao Fan; Jianfei Yang; Yuecong Xu; Lihua Xie
Venue: ECCV 2024 (main)

Abstract: "Human pose estimation (HPE) from Radio Frequency vision (RF-vision) performs human sensing using RF signals that penetrate obstacles without revealing privacy (e.g., facial information). Recently, mmWave radar has emerged as a promising RF-vision sensor, providing radar point clouds by processing RF signals. However, the mmWave radar has a limited resolution with severe noise, leading to inaccurate and inconsistent human pose estimation. This work proposes mmDiff, a novel diffusion-based pose estimator tailored for noisy radar data. Our approach aims to provide reliable guidance as conditions to diffusion models. Two key challenges are addressed by mmDiff: (1) miss-detection of parts of human bodies, which is addressed by a module that isolates feature extraction from different body parts, and (2) signal inconsistency due to environmental interference, which is tackled by incorporating prior knowledge of body structure and motion. Several modules are designed to achieve these goals, whose features work as the conditions for the subsequent diffusion model, eliminating the miss-detection and instability of HPE based on RF-vision. Extensive experiments demonstrate that mmDiff outperforms existing methods significantly, achieving state-of-the-art performances on public datasets. 1 1 The project page of mmDiff is https://fanjunqiao.github.io/mmDiff-site/."
UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues
Authors: Vandad Davoodnia; Saeed Ghorbani; Marc-André Carbonneau; Alexandre Messier; Ali Etemad
Venue: ECCV 2024 (main)

Abstract: "We introduce UPose3D, a novel approach for multi-view 3D human pose estimation, addressing challenges in accuracy and scalability. Our method advances existing pose estimation frameworks by improving robustness and flexibility without requiring direct 3D annotations. At the core of our method, a pose compiler module refines predictions from a 2D keypoints estimator that operates on a single image by leveraging temporal and cross-view information. Our novel cross-view fusion strategy is scalable to any number of cameras, while our synthetic data generation strategy ensures generalization across diverse actors, scenes, and viewpoints. Finally, UPose3D leverages the prediction uncertainty of both the 2D keypoint estimator and the pose compiler module. This provides robustness to outliers and noisy data, resulting in state-of-the-art performance in out-of-distribution settings. In addition, for in-distribution settings, UPose3D yields performance rivalling methods that rely on 3D annotated data while being the state-of-the-art among methods relying only on 2D supervision."
Learning 3D-aware GANs from Unposed Images with Template Feature Field
Authors: Xinya Chen; Hanlei Guo; Yanrui Bin; Shangzhan Zhang; Yuanbo Yang; Yujun Shen; Yue Wang; Yiyi Liao
Venue: ECCV 2024 (main)

Abstract: "Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice. This work targets learning 3D-aware GANs from unposed images, for which we propose to perform on-the-fly pose estimation of training images with a learned template feature field (). Concretely, in addition to a generative radiance field as in previous approaches, we ask the generator to also learn a field from 2D semantic features while sharing the density from the radiance field. Such a framework allows us to acquire a canonical 3D feature template leveraging the dataset mean discovered by the generative model, and further efficiently estimate the pose parameters on real data. Experimental results on various challenging datasets demonstrate the superiority of our approach over state-of-the-art alternatives from both the qualitative and the quantitative perspectives. Project page: https://XDimlab. github.io/TeFF."
TAPTR: Tracking Any Point with Transformers as Detection
Authors: Hongyang Li; Hao Zhang; Shilong Liu; Zhaoyang Zeng; Tianhe Ren; Feng Li; Lei Zhang
Venue: ECCV 2024 (main)

Abstract: "In this paper, we propose a simple yet effective approach for Tracking Any Point with TRansformers (). Based on the observation that point tracking bears a great resemblance to object detection and tracking, we borrow designs from DETR-like algorithms to address the task of TAP. In , in each video frame, each tracking point is represented as a point query, which consists of a positional part and a content part. As in DETR, each query (its position and content feature) is naturally updated layer by layer. Its visibility is predicted by its updated content feature. Queries belonging to the same tracking point can exchange information through self-attention along the temporal dimension. As all such operations are well-designed in DETR-like algorithms, the model is conceptually very simple. We also adopt some useful designs such as cost volume from optical flow models and develop simple designs to provide long temporal information while mitigating the feature drifting issue. demonstrates strong performance with state-of-the-art performance on various datasets with faster inference speed."
Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning
Authors: Shibo Jie; Yehui Tang; Jianyuan Guo; Zhi-Hong Deng; Kai Han; Yunhe Wang
Venue: ECCV 2024 (main)

Abstract: "Token compression expedites the training and inference of Vision Transformers (ViTs) by reducing the number of the redundant tokens, , pruning inattentive tokens or merging similar tokens. However, when applied to downstream tasks, these approaches suffer from significant performance drop when the compression degrees are mismatched between training and inference stages, which limits the application of token compression on off-the-shelf trained models. In this paper, we propose a model arithmetic framework to decouple the compression degrees between the two stages. In advance, we additionally perform a fast parameter-efficient self-distillation stage on the pre-trained models to obtain a small plugin, called Token Compensator (ToCom), which describes the gap between models across different compression degrees. During inference, ToCom can be directly inserted into any downstream off-the-shelf models with any mismatched training and inference compression degrees to acquire universal performance improvements without further training. Experiments on over 20 downstream tasks demonstrate the effectiveness of our framework. On CIFAR100, fine-grained visual classification, and VTAB-1k benchmark, ToCom can yield up to a maximum improvement of 2.3%, 1.5%, and 2.0% in the average performance of DeiT-B, respectively. 1 Corresponding Author."
Point-supervised Panoptic Segmentation via Estimating Pseudo Labels from Learnable Distance
Authors: Jing Li; Junsong Fan; Zhaoxiang Zhang
Venue: ECCV 2024 (main)

Abstract: "To bridge the gap between point labels and per-pixel labels, existing point-supervised panoptic segmentation methods usually estimate dense pseudo labels by assigning unlabeled pixels to corresponding instances according to rule-based pixel-to-instance distances. These distances cannot be optimized by point labels end to end and are usually suboptimal, which result in inaccurate pseudo labels. Here we propose to assign unlabeled pixels to corresponding instances based on a learnable distance. Specifically, we represent each instance as an anchor query, then predict the pixel-to-instance distance based on the cross-attention between anchor queries and pixel features through a distance branch, the predicted distance is supervised by point labels end to end. In order that each query can accurately represent the corresponding instance, we iteratively improve anchor queries through query aggregating and query enhancing processes, then improved distance results and pseudo labels are predicted with these queries. We have experimentally demonstrated the effectiveness of our approach and achieved state-of-the-art results."
BRAVE: Broadening the visual encoding of vision-language models
Authors: Oğuzhan Fatih Kar; Alessio Tonioni; Petra Poklukar; Achin Kulshrestha; Amir Zamir; Federico Tombari
Venue: ECCV 2024 (main)

Abstract: "Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due to the limited capabilities of vision encoders, e.g. “blindness” to certain image features, visual hallucination, etc. To address these issues, we study broadening the visual encoding capabilities of VLMs. We first comprehensively benchmark several vision encoders with different inductive biases for solving VLM tasks. We observe that there is no single encoding configuration that consistently achieves top performance across different tasks, and encoders with different biases can perform surprisingly similarly. Motivated by this, we introduce a method, named , that consolidates features from multiple frozen encoders into a more versatile representation that can be directly fed as the input to a frozen LM. achieves state-of-the-art performance on a broad range of captioning and VQA benchmarks and significantly reduces the aforementioned issues of VLMs, while requiring a smaller number of trainable parameters than existing methods and having a more compressed representation. Our results highlight the potential of incorporating different visual biases for a more broad and contextualized visual understanding of VLMs."
HUMOS: Human Motion Model Conditioned on Body Shape
Authors: Shashank Tripathi; Omid Taheri; Christoph Lassner; Michael J. Black; Daniel Holden; Carsten Stoll
Venue: ECCV 2024 (main)

Abstract: "Generating realistic human motion is crucial for many computer vision and graphics applications. The rich diversity of human body shapes and sizes significantly influences how people move. However, existing motion models typically overlook these differences, using a normalized, average body instead. This results in a homogenization of motion across human bodies, with motions not aligning with their physical attributes, thus limiting diversity. To address this, we propose a novel approach to learn a generative motion model conditioned on body shape. We demonstrate that it is possible to learn such a model from unpaired training data using cycle consistency, intuitive physics, and stability constraints that model the correlation between identity and movement. The resulting model produces diverse, physically plausible, and dynamically stable human motions that are quantitatively and qualitatively more realistic than existing state of the art. More details are available on our project page ."
Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields
Authors: Yonggan Fu; Huaizhi Qu; Zhifan Ye; Chaojian Li; Kevin Zhao; Yingyan (Celine) Lin
Venue: ECCV 2024 (main)

Abstract: "Recent breakthroughs in Neural Radiance Fields (NeRFs) have sparked significant demand for their integration into real-world 3D applications. However, the varied functionalities required by different 3D applications often necessitate diverse NeRF models with various pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-error experiments. Drawing inspiration from the generalization capability and adaptability of emerging foundation models, our work aims to develop one general-purpose NeRF for handling diverse 3D tasks. We achieve this by proposing a framework called , which is capable of (1) generalizable 3D reconstruction and zero-shot multitask scene understanding, and (2) adaptability to diverse downstream 3D applications such as real-time rendering and scene editing. Our key insight is that an image-based rendering pipeline, with accurate geometry and appearance estimation, can lift 2D image features into their 3D counterparts, thus extending widely explored 2D tasks to the 3D world in a generalizable manner. Specifically, our features a general-purpose NeRF model using image-based rendering with two decoupled branches: one complex transformer-based branch that progressively fuses geometry and appearance features for accurate geometry estimation, and one lightweight branch for predicting blending weights of source views. This design achieves state-of-the-art (SOTA) generalizable 3D surface reconstruction quality with blending weights reusable across diverse tasks for zero-shot multitask scene understanding. In addition, it can enable real-time rendering after baking the complex geometry branch into meshes, swift adaptation to achieve SOTA generalizable 3D understanding performance, and seamless integration with 2D diffusion models for text-guided 3D editing. Our code is available at: https://github.com/GATECH-EIC/Omni-Recon."
MVDiffHD: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
Authors: Shitao Tang; Jiacheng Chen; Dilin Wang; Chengzhou Tang; Fuyang Zhang; Yuchen Fan; Vikas Chandra; Yasutaka Furukawa; Rakesh Ranjan
Venue: ECCV 2024 (main)

Abstract: "This paper presents a neural architecture for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A “pose-free architecture” where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A “view dropout strategy” that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time. We use the Objaverse for training and the Google Scanned Objects for evaluation with standard novel view synthesis and 3D reconstruction metrics, where significantly outperforms the current state of the arts. We also demonstrate a text-to-3D application example by combining with a text-to-image generative model. The project page is at https://mvdiffusion-plusplus.github.io."
FlowCon: Out-of-Distribution Detection using Flow-based Contrastive Learning
Authors: Saandeep Aathreya; Shaun Canavan
Venue: ECCV 2024 (main)

Abstract: "Identifying Out-of-distribution (OOD) data is becoming increasingly critical as the real-world applications of deep learning methods expand. Post-hoc methods modify softmax scores fine-tuned on outlier data or leverage intermediate feature layers to identify distinctive patterns between In-Distribution (ID) and OOD samples. Other methods focus on employing diverse OOD samples to learn discrepancies between ID and OOD. These techniques, however, are typically dependent on the quality of the outlier samples assumed. Density-based methods explicitly model class-conditioned distributions but this requires long training time or retraining the classifier. To tackle these issues, we introduce FlowCon, a new density-based OOD detection technique. Our main innovation lies in efficiently combining the properties of normalizing flow with supervised contrastive learning, ensuring robust representation learning with tractable density estimation. Empirical evaluation shows the enhanced performance of our method across common vision datasets such as CIFAR-10 and CIFAR-100 pretrained on ResNet18 and WideResNet classifiers. We also perform quantitative analysis using likelihood plots and qualitative visualization using UMAP embeddings and demonstrate the robustness of the proposed method under various OOD contexts. Code can be found at https://github.com/saandeepa93/FlowCon_OOD."
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
Authors: Archana Swaminathan; Anubhav Gupta; Kamal Gupta; Shishira R Maiya; Vatsal Agarwal; Abhinav Shrivastava
Venue: ECCV 2024 (main)

Abstract: "Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories, which can limit their practical use. In this work, we introduce , a novel approach for representing dynamic 3D objects. Our method involves observing the object at distinct time steps or “states” and conditioning a hypernetwork on the current state, using this to parameterize our NeRF. This approach allows us to learn a view-invariant latent representation for each state. We further demonstrate that by interpolating between these states, we can generate novel articulation configurations in 3D space that were previously unseen. Our experimental results highlight the effectiveness of our method in articulating objects in a manner that is independent of the viewing angle and joint configuration. Notably, our approach outperforms previous methods that rely on motion information for articulation registration."
Un-EVIMO: Unsupervised Event-based Independent Motion Segmentation
Authors: Ziyun Wang; Jinyuan Guo; Kostas Daniilidis
Venue: ECCV 2024 (main)

Abstract: "Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption. Because of these properties, they are well-suited for processing fast motions that require rapid reactions. Event cameras have shown competitive performance in unsupervised optical flow estimation. However, performance in detecting independently moving objects (IMOs) is lacking behind, although event-based methods would be suited for this task based on their low latency and HDR properties. Previous approaches to event-based IMO segmentation heavily depended on labeled data. However, biological vision systems have developed the ability to avoid moving objects through daily tasks without using explicit labels. In this work, we propose the first event framework that generates IMO pseudo-labels using geometric constraints. Due to its unsupervised nature, our method can flexibly handle a non-predetermined arbitrary number of objects and is easily scalable to datasets where expensive IMO labels are not readily available. Our approach shows competitive performance on the EVIMO dataset compared with supervised methods, both quantitatively and qualitatively. See the project website for details: https://www.cis.upenn.edu/~ziyunw/un_evimo/."
Seeing the Unseen: A Frequency Prompt Guided Transformer for Image Restoration
Authors: Shihao Zhou; Jinshan Pan; Jinglei Shi; Duosheng Chen; Lishen Qu; Jufeng Yang
Venue: ECCV 2024 (main)

Abstract: "How to explore useful features from images as prompts to guide the deep image restoration models is an effective way to solve image restoration. In contrast to mining spatial relations within images as prompt, which leads to characteristics of different frequencies being neglected and further remaining subtle or undetectable artifacts in the restored image, we develop a Frequency Prompting image restoration method, dubbed FPro, which can effectively provide prompt components from a frequency perspective to guild the restoration model address these differences. Specifically, we first decompose input features into separate frequency parts via dynamically learned filters, where we introduce a gating mechanism for suppressing the less informative elements within the kernels. To propagate useful frequency information as prompt, we then propose a dual prompt block, consisting of a low-frequency prompt modulator (LPM) and a high-frequency prompt modulator (HPM), to handle signals from different bands respectively. Each modulator contains a generation process to incorporate prompting components into the extracted frequency maps, and a modulation part that modifies the prompt feature with the guidance of the decoder features. Experimental results on several popular datasets have demonstrated the favorable performance of our pipeline against SOTA methods on 5 image restoration tasks, including deraining, deraindrop, demoiréing, deblurring, and dehazing. The source code is available at https://github.com/joshyZhou/FPro."
CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians
Authors: Yang Liu; Chuanchen Luo; Lue Fan; Naiyan Wang; Junran Peng; Zhaoxiang Zhang
Venue: ECCV 2024 (main)

Abstract: "The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Specifically, the global scene prior and adaptive training data selection enables efficient training and seamless fusion. Based on fused Gaussian primitives, we generate different detail levels through compression, and realize fast rendering across various scales through the proposed block-wise detail levels selection and aggregation strategy. Extensive experimental results on large-scale scenes demonstrate that our approach attains state-of-the-art rendering quality, enabling consistent real-time rendering of large-scale scenes across vastly different scales. Our project page is available at https://dekuliutesla.github.io/citygs/."
Bayesian Evidential Deep Learning for Online Action Detection
Authors: Hongji Guo; Hanjing Wang; Qiang Ji
Venue: ECCV 2024 (main)

Abstract: "Online action detection aims at identifying the ongoing action in a streaming video without seeing the future. Timely and reliable response is critical for real-world applications. In this paper, we introduce Bayesian Evidential Deep Learning (BEDL), an efficient and generalizable framework for online action detection and uncertainty quantification. Specifically, we combine Bayesian neural networks and evidential deep learning by a teacher-student architecture. The teacher model is built in a Bayesian manner and transfers its mutual information and distribution to the student model through evidential deep learning. In this way, the student model can make accurate online inference while efficiently quantifying the uncertainty. Compared to existing evidential deep learning methods, BEDL estimates uncertainty more accurately by leveraging the Bayesian teacher model. In addition, we designed an attention module for active OAD, which actively selects important features based on the Bayesian mutual information instead of using all the features. We evaluated BEDL on benchmark datasets including THUMPS’14, TVSeries, and HDD. BEDL achieves competitive performance while keeping efficient inference. Extensive ablation studies demonstrate the effectiveness of each component. To verify the uncertainty quantification, we perform experiments of online anomaly detection with different types of uncertainties."