GenAI4ELab/papercli-papers huggingface dataset:
3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
Authors: Marc Badger; Yufu Wang; Adarsh Modh; Ammon Perkes; Nikos Kolotouros; Bernd G. Pfrommer; Marc F. Schmidt; Kostas Daniilidis
Venue: ECCV 2020 (main)

Abstract: Model, and Shape Recovery from a Single View","Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh."
We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos
Authors: Alex Andonian; Camilo Fosco; Mathew Monfort; Allen Lee; Rogerio Feris; Carl Vondrick; Aude Oliva
Venue: ECCV 2020 (main)

Abstract: Identifying common patterns among events is a key capability for human and machine perception, as it underlies intelligent decision making. Here, we propose an approach for learning semantic relational set abstractions on videos, inspired by human learning. Our model combines visual features as input with natural language supervision to generate high-level representations of similarities across a set of videos. This allows our model to perform cognitive tasks such as set abstraction (what is in common among a set of videos?), set completion (which new video goes well with the set?), and odd one out detection (which video does not belong in the set?). Experiments on two video benchmarks, Kinetics and Multi-Moments in Time, show that robust and versatile representations emerge when learning to recognize commonalities among sets. We compare our model to several baseline algorithms and show that significant improvements result from explicitly learning relational abstractions with semantic supervision."
Joint Optimization for Multi-Person Shape Models from Markerless 3D-Scans
Authors: Samuel Zeitvogel; Johannes Dornheim; Astrid Laubenheimer
Venue: ECCV 2020 (main)

Abstract: We propose a markerless end-to-end training framework for parametric 3D human shape models. The training of statistical 3D human shape models with minimal supervision is an important problem in computer vision. Contrary to prior work, the whole training process (i) uses a differentiable shape model surface and (ii) is trained end-to-end by jointly optimizing all parameters of a single, self-contained objective that can be solved with slightly modified off-the-shelf non-linear least squares solvers. The training process only requires a compact model definition and an off-the-shelf 2D RGB pose estimator. No pre-trained shape models are required. For training (iii) a medium-sized dataset of approximately 1000 low-resolution human body scans is sufficient to achieve competitive performance on the challenging FAUST surface correspondence benchmark. The training and evaluation code will be made available for research purposes to facilitate end-to-end shape model training on novel datasets with minimal setup cost."
Accurate RGB-D Salient Object Detection via Collaborative Learning
Authors: Wei Ji; Jingjing Li; Miao Zhang; Yongri Piao; Huchuan Lu
Venue: ECCV 2020 (main)

Abstract: Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are still two limitations. One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, using an additional depth-network to extract depth features might lead to high computation and storage cost. The reliance on depth inputs during testing also limits the practical applications of current RGB-D models. In this paper, we propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way, which solves those problems tactfully. The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries. Depth and saliency learning is innovatively integrated into the high-level feature learning process in a mutual-benefit manner. This strategy enables the network to be free of using extra depth networks and depth inputs to make inference. To this end, it makes our model more lightweight, faster and more versatile. Experiment results on seven benchmark datasets show its superior performance."
Finding Your (3D) Center: 3D Object Detection Using a Learned Loss
Authors: David Griffiths; Jan Boehm; Tobias Ritschel
Venue: ECCV 2020 (main)

Abstract: Massive semantically labeled datasets are readily available for 2D images, however, are much harder to achieve for 3D scenes. Objects in 3D repositories like ShapeNet are labeled, but regrettably only in isolation, so without context. 3D scenes can be acquired by range scanners on city-level scale, but much fewer with semantic labels. Addressing this disparity, we introduce a new optimization procedure, which allows training for 3D detection with raw 3D scans while using as little as 5\,\% of the object labels and still achieve comparable performance. Our optimization uses two networks. A scene network maps an entire 3D scene to a set of 3D object centers. As we assume the scene not to be labeled by centers, no classic loss, such as Chamfer can be used to train it. Instead, we use another network to emulate the loss. This loss network is trained on a small labeled subset and maps a non-centered 3D object in the presence of distractions to its own center. This function is very similar -- and hence can be used instead of -- the gradient the supervised loss would provide. Our evaluation documents competitive fidelity at a much lower level of supervision, respectively higher quality at comparable supervision. Supplementary material can be found at: https://dgriffiths3.github.io."
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection
Authors: Ganlong Zhao; Guanbin Li; Ruijia Xu; Liang Lin
Venue: ECCV 2020 (main)

Abstract: Object detectors are usually trained with large amount of labeled data, which is expensive and labor-intensive. Pre-trained detectors applied to unlabeled dataset always suffer from the difference of dataset distribution, also called domain shift. Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier (RPC) in the endemic two-stage detectors (e.g., Faster RCNN) demonstrate significantly different transferability when facing large domain gap. The region classifier shows preferable performance but is limited without RPN's high-quality proposals while simple alignment in the backbone network is not effective enough for RPN adaptation. We delve into the consistency and the difference of RPN and RPC, treat them individually and leverage high-confidence output of one as mutual guidance to train the other. Moreover, the samples with low-confidence are used for discrepancy calculation between RPN and RPC and minimax optimization. Extensive experimental results on various scenarios have demonstrated the effectiveness of our proposed method in both domain-adaptive region proposal generation and object detection. Code is available at https://github.com/GanlongZhao/CST_DA_detection."
Two Stream Active Query Suggestion for Active Learning in Connectomics
Authors: Zudi Lin; Donglai Wei; Won-Dong Jang; Siyan Zhou; Xupeng Chen; Xueying Wang; Richard Schalek; Daniel Berger; Brian Matejek; Lee Kamentsky; Adi Peleg; Daniel Haehn; Thouis Jones; Toufiq Parag; Jeff Lichtman; Hanspeter Pfister
Venue: ECCV 2020 (main)

Abstract: For large-scale vision tasks in biomedical images, the labeled data is often limited to train effective deep models. Active learning is a common solution, where a query suggestion method selects representative unlabeled samples for annotation, and the new labels are used to improve the base model. However, most query suggestion models optimize their learnable parameters only on the limited labeled data and consequently become less effective for the more challenging unlabeled data. To tackle this, we propose a two-stream active query suggestion approach. In addition to the supervised feature extractor, we introduce an unsupervised one optimized on all raw images to capture diverse image features, which can later be improved by fine-tuning on new labels. As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics. With the framework, we curate, to our best knowledge, the largest connectomics dataset with dense synapses and mitochondria annotation. On this new dataset, our method outperforms previous state-of-the-art methods by 3.1% for synapse and 3.8% for mitochondria in terms of region-of-interest proposal accuracy. We also apply our method to image classification, where it outperforms previous approaches on CIFAR-10 under the same limited annotation budget. The project page is https://zudi-lin.github.io/projects/#two_stream_active."
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
Authors: Jiahui Lei; Srinath Sridhar; Paul Guerrero; Minhyuk Sung; Niloy Mitra; Leonidas J. Guibas
Venue: ECCV 2020 (main)

Abstract: We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape reconstruction from multiple views uses discrete representations such as point clouds or voxels, while continuous surface generation approaches lack multi-view consistency. We address these issues by designing neural networks capable of generating high-quality parametric 3D surfaces which are also consistent between views. Furthermore, the generated 3D surfaces preserve accurate image pixel to 3D surface point correspondences, allowing us to lift texture information to reconstruct shapes with rich geometry and appearance. Our method is supervised and trained on a public dataset of shapes from common object categories. Quantitative results indicate that our method significantly outperforms previous work, while qualitative results demonstrate the high quality of our reconstructions."
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
Authors: Mai Bui; Tolga Birdal; Haowen Deng; Shadi Albarqouni; Leonidas Guibas; Slobodan Ilic; Nassir Navab
Venue: ECCV 2020 (main)

Abstract: We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses. In highly ambiguous environments, which can easily arise due to symmetries and repetitive structures in the scene, computing one plausible solution (what most state-of-the-art methods currently regress) may not be sufficient. Instead we predict multiple camera pose hypotheses as well as the respective uncertainty for each prediction. Towards this aim, we use Bingham distributions, to model the orientation of the camera pose, and a multivariate Gaussian to model the position, with an end-to-end deep neural network. By incorporating a Winner-Takes-All training scheme, we finally obtain a mixture model that is well suited for explaining ambiguities in the scene, yet does not suffer from mode collapse, a common problem with mixture density networks. We introduce a new dataset specifically designed to foster camera localization research in ambiguous environments and exhaustively evaluate our method on synthetic as well as real data on both ambiguous scenes and on non-ambiguous benchmark datasets. We plan to release our code and dataset under multimodal3dvision.github.io."
Modeling Artistic Workflows for Image Generation and Editing
Authors: Hung-Yu Tseng; Matthew Fisher; Jingwan Lu; Yijun Li; Vladimir Kim; Ming-Hsuan Yang
Venue: ECCV 2020 (main)

Abstract: People often create art by following an artistic workflow involving multiple stages that inform the overall design. If an artist wishes to modify an earlier decision, significant work may be required to propagate this new decision forward to the final artwork. Motivated by the above observations, we propose a generative model that follows a given artistic workflow, enabling both multi-stage image generation as well as multi-stage image editing of an existing piece of art. Furthermore, for the editing scenario, we introduce an optimization process along with learning-based regularization to ensure the edited image produced by the model closely aligns with the originally provided image. Qualitative and quantitative results on three different artistic datasets demonstrate the effectiveness of the proposed framework on both image generation and editing tasks."
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks
Authors: Sangpil Kim; Hyung-gun Chi; Xiao Hu; Qixing Huang; Karthik Ramani
Venue: ECCV 2020 (main)

Abstract: We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components. The dataset enables data-driven feature learn-ing for mechanical components. Exploring the shape descriptor for mechanical components is essential to computer vision and manufacturing applications. However, not much attention has been given on creating an-notated mechanical components datasets on a large-scale. This is because acquiring 3D models is challenging and annotating mechanical components requires engineering knowledge. Our main contributions are the creation of a large-scale annotated mechanical component benchmark, defining hierarchy taxonomy of mechanical components, and benchmark-ing the effectiveness of deep learning shape classifiers on the mechanical components. We created an annotated dataset and benchmarked seven state-of-the-art deep learning classification methods in three categories, namely: (1) point clouds, (2) volumetric representation in voxel grids, and (3) view-based representation."
Hidden Footprints: Learning Contextual Walkability from 3D Human Trails
Authors: Jin Sun; Hadar Averbuch-Elor; Qianqian Wang; Noah Snavely
Venue: ECCV 2020 (main)

Abstract: Predicting where people can walk in a scene is important for many tasks, including autonomous driving systems and human behavior analysis. Yet learning a computational model for this purpose is challenging due to semantic ambiguity and a lack of labeled data: current datasets only have labels on where people $ extit{are}$, not where they $ extit{could be}$. We tackle this problem by leveraging information from existing datasets, without any additional labeling. We first augment the set of valid walkable regions by propagating person observations between images, utilizing 3D information and temporal coherence, leading to $ extit{Hidden Footprints}$. We then design a training strategy that combines a class-balanced classification loss with a contextual adversarial loss to learn from sparse observations, thus obtaining a model that predicts a walkability map of a given scene. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance against baselines and state-of-the-art models."
Self-Supervised Learning of Audio-Visual Objects from Video
Authors: Triantafyllos Afouras; Andrew Owens; Joon Son Chung; Andrew Zisserman
Venue: ECCV 2020 (main)

Abstract: Our objective is to transform a video into a set of discrete audio-visual objects using self-supervised learning. To this end we introduce a model that uses attention to localize and group sound sources, and optical flow to aggregate information over time. We demonstrate the effectiveness of the audio-visual object embeddings that our model learns by using them for four downstream speech-oriented tasks: (a) multi-speaker sound source separation, (b) localizing and tracking speakers, (c) correcting misaligned audio-visual data, and (d) active speaker detection. Using our representation, these tasks can be solved entirely by training on unlabeled video, without the aid of object detectors. We also demonstrate the generality of our method by applying it to non-human speakers, including cartoons and puppets. Our model significantly outperforms other self-supervised approaches, and obtains performance competitive with methods that use supervised face detection."
GAN-based Garment Generation Using Sewing Pattern Images
Authors: Yu Shen; Junbang Liang; Ming C. Lin
Venue: ECCV 2020 (main)

Abstract: The generation of realistic apparel model has become increasingly popular as a result of the rapid pace of change in fashion trends and the growing need for garment models in various applications such as virtual try-on. For such application requirements, it is important to have a general cloth model that can represent a diverse set of garments. Previous studies often make certain assumptions about the garment, such as the topology or suited body shape. We propose a unified method using the generative network. Our model is applicable to different garment topologies with different sewing patterns and fabric materials. We also develop a novel image representation of garment models, and a reliable mapping algorithm between the general garment model and the image representation that can regularize the data representation of the cloth. Using this special intermediate image representation, the generated garment model can be easily retargeted to another body, enabling garment customization. In addition, a large garment appearance dataset is provided for use in garment reconstruction, garment capturing, and other applications. We demonstrate that our generative model has high reconstruction accuracy and can provide rich variations of virtual garments."
Style Transfer for Co-Speech Gesture Animation: A Multi-Speaker Conditional-Mixture Approach
Authors: Chaitanya Ahuja; Dong Won Lee; Yukiko I. Nakano; Louis-Philippe Morency
Venue: ECCV 2020 (main)

Abstract: How can we teach robots or virtual assistants to gesture naturally? Can we go further and adapt the gesturing style to follow a specific speaker? Gestures that are naturally timed with corresponding speech during human communication are called co-speech gestures. A key challenge, called gesture style transfer, is to learn a model that generates these gestures for a speaking agent 'A' in the gesturing style of a target speaker 'B'. A secondary goal is to simultaneously learn to generate co-speech gestures for multiple speakers while remembering what is unique about each speaker. We call this challenge style preservation. In this paper, we propose a new model, named Mix-StAGE, which trains a single model for multiple speakers while learning unique style embeddings for each speaker's gestures in an end-to-end manner. A novelty of Mix-StAGE is to learn a mixture of generative models which allows for conditioning on the unique gesture style of each speaker. As Mix-StAGE disentangles style and content of gestures, gesturing styles for the same input speech can be altered by simply switching the style embeddings. Mix-StAGE also allows for style preservation when learning simultaneously from multiple speakers. We also introduce a new dataset, Pose-Audio-Transcript-Style (PATS), designed to study gesture generation and style transfer. Our proposed Mix-StAGE model significantly outperforms the previous state-of-the-art approach for gesture generation and provides a path towards performing gesture style transfer across multiple speakers. Link to code, data, and videos: http://chahuja.com/mix-stage"
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
Authors: Rui Huang; Wanyue Zhang; Abhijit Kundu; Caroline Pantofaru; David A Ross; Thomas Funkhouser; Alireza Fathi
Venue: ECCV 2020 (main)

Abstract: Detecting objects in 3D LiDAR data is a core technology for autonomous driving and other robotics applications. Although LiDAR data is acquired over time, most of the 3D object detection algorithms propose object bounding boxes independently for each frame and neglect the useful information available in the temporal domain. To address this problem, in this paper we propose a sparse LSTM-based multi-frame 3d object detection algorithm. We use a U-Net style 3D sparse convolution network to extract features for each frame's LiDAR point-cloud. These features are fed to the LSTM module together with the hidden and memory features from last frame to predict the 3d objects in the current frame as well as hidden and memory features that are passed to the next frame. Experiments on the Waymo Open Dataset show that our algorithm outperforms the traditional frame by frame approach by 7.5% mAP@0.7 and other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds."
Monotonicity Prior for Cloud Tomography
Authors: Tamar Loeub; Aviad Levis; Vadim Holodovsky; Yoav Y. Schechner
Venue: ECCV 2020 (main)

Abstract: We introduce a differentiable monotonicity prior, useful to express signals of monotonic tendency. An important natural signal of this tendency is the optical extinction coefficient, as a function of altitude in a cloud. Cloud droplets become larger as vapor condenses on them in an updraft. Reconstruction of the volumetric structure of clouds is important for climate research. Data for such reconstruction is multi-view images taken simultaneously of each cloud. This acquisition mode is expected by upcoming future spaceborne imagers. We achieve three-dimensional volumetric reconstruction through stochastic scattering tomography, which is based on optimization of a cost function. Part of the cost is the monotonicity prior, which helps to improve the reconstruction quality. The stochastic tomography is based on Monte-Carlo radiative transfer. It is formulated and implemented in a coarse-to-fine form, making it scalable to large fields."
Learning Trailer Moments in Full-Length Movies with Co-Contrastive Attention
Authors: Lezi Wang; Dong Liu; Rohit Puri; Dimitris N. Metaxas
Venue: ECCV 2020 (main)

Abstract: A movie's key moments stand out of the screenplay to grab an audience's attention and make movie browsing efficient. But a lack of annotations makes the existing approaches not applicable to movie key moment detection. To get rid of human annotations, we leverage the officially-released trailers as the weak supervision to learn a model that can detect the key moments from full-length movies. We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to be scored higher than the uncorrelated moments. Additionally, we propose a Contrastive Attention module to enhance the feature representations such that the comparative contrast between features of the key and non-key moments are maximized. We construct the first movie-trailer dataset, and the proposed Co-Attention assisted ranking network shows superior performance even over the supervisedootnote{The term ""supervised"" refers to the approach with access to the manual ground-truth annotations for training.} approach. The effectiveness of our Contrastive Attention module is also demonstrated by the performance improvement over the state-of-the-art on the public benchmarks."