mtybilly/Instruct2DS huggingface dataset:
CMT: Context-Matching-Guided Transformer for 3D Tracking in Point Clouds
Authors: Yunyao Mao, Wengang Zhou, Min Wang, Houqiang Li Zhiyang Guo
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "How to effectively match the target template features with the search area is the core problem in point-cloud-based 3D single object tracking. However, in the literature, most of the methods focus on devising sophisticated matching modules at point-level, while overlooking the rich spatial context information of points. To this end, we propose Context-Matching-Guided Transformer (CMT), a Siamese tracking paradigm for 3D single object tracking. In this work, we first leverage the local distribution of points to construct a horizontally rotation-invariant contextual descriptor for both the template and the search area. Then, a novel matching strategy based on shifted windows is designed for such descriptors to effectively measure the template-search contextual similarity. Furthermore, we introduce a target-specific transformer and a spatial-aware orientation encoder to exploit the target-aware information in the most contextually relevant template points, thereby enhancing the search feature for a better target proposal. We conduct extensive experiments to verify the merits of our proposed CMT and report a series of new state-of-the-art records on three widely-adopted datasets."
Towards Generic 3D Tracking in RGBD Videos: Benchmark and Baseline
Authors: Zhongqun Zhang, Zhe Li, Hyung Jin Chang, Aleš Leonardis, Feng Zheng Jinyu Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Tracking in 3D scenes is gaining momentum because of its numerous applications in robotics, autonomous driving, and scene understanding. Currently, 3D tracking is limited to specific model-based approaches involving point clouds, which impedes 3D trackers from applying in natural 3D scenes. RGBD sensors provide a more reasonable and acceptable solution for 3D object tracking due to their readily available synchronised color and depth information. Thus, in this paper, we investigate a novel problem: is it possible to track a generic (class-agnostic) 3D object in RGBD videos and predict 3D bounding boxes of the object of interest? To inspire further research on this topic, we newly construct a standard benchmark for generic 3D object tracking, ‘Track-it-in-3D’, which contains 300 RGBD video sequences with dense 3D annotations and corresponding evaluation protocols. Furthermore, we propose an effective tracking baseline to estimate 3D bounding boxes for arbitrary objects in RGBD videos, by fusing appearance and spatial information effectively. The dataset and codes will be publicly available."
Hierarchical Latent Structure for Multi-modal Vehicle Trajectory Forecasting
Authors: KyoungWook Min Dooseop Choi
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and has nice manifold representations. However, when applied to image reconstruction and synthesis tasks, VAE shows the limitation that the generated sample tends to be blurry. We observe that a similar problem, in which the generated trajectory is located between adjacent lanes, often arises in VAE-based trajectory forecasting models. To mitigate this problem, we introduce a hierarchical latent structure into the VAE-based forecasting model. Based on the assumption that the trajectory distribution can be approximated as a mixture of simple distributions (or modes), the low-level latent variable is employed to model each mode of the mixture and the high-level latent variable is employed to represent the weights for the modes. To model each mode accurately, we condition the low-level latent variable using two lane-level context vectors computed in novel ways, one corresponds to vehicle-lane interaction and the other to vehicle-vehicle interaction. The context vectors are also used to model the weights via the proposed mode selection network. To evaluate our forecasting model, we use two large-scale real-world datasets. Experimental results show that our model is not only capable of generating clear multi-modal trajectory distributions but also outperforms the state-of-the-art (SOTA) models in terms of prediction accuracy."
AiATrack: Attention in Attention for Transformer Visual Tracking
Authors: Chunluan Zhou, Chao Ma, Xinggang Wang, Junsong Yuan Shenyuan Gao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Transformer trackers have achieved impressive advancements recently, where the attention mechanism plays an important role. However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement. To address this issue, we propose an attention in attention (AiA) module, which enhances appropriate correlations and suppresses erroneous ones by seeking consensus among all correlation vectors. Our AiA module can be readily applied to both self-attention blocks and cross-attention blocks to facilitate feature aggregation and information propagation for visual tracking. Moreover, we propose a streamlined Transformer tracking framework, dubbed AiATrack, by introducing efficient feature reuse and target-background embeddings to make full use of temporal references. Experiments show that our tracker achieves state-of-the-art performance on six tracking benchmarks while running at a real-time speed."
Disentangling Architecture and Training for Optical Flow
Authors: Charles Herrmann, Fitsum Reda, Michael Rubinstein, David J. Fleet, William T. Freeman Deqing Sun
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "How important are training details and datasets to recent optical flow models like RAFT? And do they generalize? To explore these questions, rather than develop a new model, we revisit three prominent models, PWC-Net, IRR-PWC and RAFT, with a common set of modern training techniques, and observe significantly better performance, demonstrating the importance and generality of these training details. Our newly trained PWC-Net and IRR-PWC models show surprisingly large improvements, up to 30% versus original published results on Sintel and KITTI 2015 benchmarks. They outperform the more recent Flow1D on KITTI 2015 while being 3× faster during inference. Our newly trained RAFT achieves an Fl-all score of 4.31% on KITTI 2015, more accurate than all published optical flow methods. Our results demonstrate the benefits of separating the contributions of models, training techniques and datasets when analyzing performance gains of optical flow methods. Our source code will be publicly available."
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Authors: Philipp Scholze, Andrés Bruhn Jenny Schmalfuss
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected. Although adversarial attacks offer a useful tool to perform such an analysis, current attacks on optical flow methods focus on real-world attacking scenarios rather than a worst case robustness assessment. Hence, in this work, we propose a novel adversarial attack - the Perturbation-Constrained Flow Attack (PCFA) - that emphasizes destructivity over applicability as a real-world attack. PCFA is a global attack that optimizes adversarial perturbations to shift the predicted flow towards a specified target flow, while keeping the L2 norm of the perturbation below a chosen bound. Our experiments demonstrate PCFA’s applicability in white- and black-box settings, and show it finds stronger adversarial samples than previous attacks. Based on these strong samples, we provide the first joint ranking of optical flow methods considering both prediction quality and adversarial robustness, which reveals state-of-the-art methods to be particularly vulnerable. Code is available at https://github.com/cv-stuttgart/PCFA."
Robust Landmark-Based Stent Tracking in X-Ray Fluoroscopy
Authors: Yikang Liu, Li Chen, Eric Z. Chen, Xiao Chen, Shanhui Sun Luojie Huang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In clinical procedures of angioplasty (i.e., open clogged coronary arteries), devices such as balloons and stents need to be placed and expanded in arteries under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose, the resulting images are often noisy. To check the correct placement of these devices, typically multiple motion-compensated frames are averaged to enhance the view. Therefore, device tracking is a necessary procedure for this purpose. Even though angioplasty devices are designed to have radiopaque markers for the ease of tracking, current methods struggle to deliver satisfactory results due to the small marker size and complex scenes in angioplasty. In this paper, we propose an end-to-end deep learning framework for single stent tracking, which consists of three hierarchical modules: a U-Net for landmark detection, a ResNet for stent proposal and feature extraction, and a graph convolutional neural network for stent tracking that temporally aggregates both spatial information and appearance features. The experiments show that our method performs significantly better in detection compared with the state-of-the-art point-based tracking models. In addition, its fast inference speed satisfies clinical requirements."
Social ODE: Multi-agent Trajectory Forecasting with Neural Ordinary Differential Equations
Authors: Hao Wang, Dimitris N. Metaxas Song Wen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Multi-agent trajectory forecasting has recently attracted a lot of attention due to its widespread applications including autonomous driving. Most previous methods use RNNs or Transformers to model agent dynamics in the temporal dimension and social pooling or GNNs to model interactions with other agents; these approaches usually fail to learn the underlying continuous temporal dynamics and agent interactions explicitly. To address these problems, we propose Social ODE which explicitly models temporal agent dynamics and agent interactions. Our approach leverages Neural ODEs to model continuous temporal dynamics, and incorporates distance, interaction intensity, and aggressiveness estimation into agent interaction modeling in latent space. We show in extensive experiments that our Social ODE approach compares favorably with state-of-the-art, and more importantly, can successfully avoid sudden obstacles and effectively control the motion of the agent, while previous methods often fail in such cases."
Social-SSL: Self-Supervised Cross-Sequence Representation Learning Based on Transformers for Multi-agent Trajectory Prediction
Authors: Yan-Kai Wang, Hao-Siang Lin, Hong-Han Shuai, Lai-Kuan Wong, Wen-Huang Cheng Li-Wu Tsao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Earlier trajectory prediction approaches focus on ways of capturing sequential structures among pedestrians by using recurrent networks, which is known to have some limitations in capturing long sequence structures. To address this limitation, some recent works proposed Transformer-based architectures, which are built with attention mechanisms. However, these Transformer-based networks are trained end-to-end without capitalizing on the value of pre-training. In this work, we propose Social-SSL that captures cross-sequence trajectory structures via self-supervised pre-training, which plays a crucial role in improving both data efficiency and generalizability of Transformer networks for trajectory prediction. Specifically, Social-SSL models the interaction and motion patterns with three pretext tasks: interaction type prediction, closeness prediction, and masked cross-sequence to sequence pre-training. Comprehensive experiments show that Social-SSL outperforms the state-of-the-art methods by at least 12% and 20% on ETH/UCY and SDD datasets in terms of Average Displacement Error and Final Displacement Error."
Diverse Human Motion Prediction Guided by Multi-level Spatial-Temporal Anchors
Authors: Yu-Xiong Wang, Liang-Yan Gui Sirui Xu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Predicting diverse human motions given a sequence of historical poses has received increasing attention. Despite rapid progress, existing work captures the multi-modal nature of human motions primarily through likelihood-based sampling, where the mode collapse has been widely observed. In this paper, we propose a simple yet effective approach that disentangles randomly sampled codes with a deterministic learnable component named anchors to promote sample precision and diversity. Anchors are further factorized into spatial anchors and temporal anchors, which provide attractively interpretable control over spatial-temporal disparity. In principle, our spatial-temporal anchor-based sampling (STARS) can be applied to different motion predictors. Here we propose an interaction-enhanced spatial-temporal graph convolutional network (IE-STGCN) that encodes prior knowledge of human motions (e.g., spatial locality), and incorporate the anchors into it. Extensive experiments demonstrate that our approach outperforms state of the art in both stochastic and deterministic prediction, suggesting it as a unified framework for modeling human motions. Our code and pretrained models are available at https://github.com/Sirui-Xu/STARS."
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction
Authors: Jin-Hwi Park, Hae-Gon Jeon Inhwan Bae
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Modeling the dynamics of people walking is a problem of long-standing interest in computer vision. Many previous works involving pedestrian trajectory prediction define a particular set of individual actions to implicitly model group actions. In this paper, we present a novel architecture named GP-Graph which has collective group representations for effective pedestrian trajectory prediction in crowded environments, and is compatible with all types of existing approaches. A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations. To do this, GP-Graph first learns to assign each pedestrian into the most likely behavior group. Using this assignment information, GP-Graph then forms both intra- and inter-group interactions as graphs, accounting for human-human relations within a group and group-group relations, respectively. To be specific, for the intra-group interaction, we mask pedestrian graph edges out of an associated group. We also propose group pooling&unpooling operations to represent a group with multiple pedestrians as one graph node. Lastly, GP-Graph infers a probability map for socially-acceptable future trajectories from the integrated features of both group interactions. Moreover, we introduce a group-level latent vector sampling to ensure collective inferences over a set of possible future trajectories. Extensive experiments are conducted to validate the effectiveness of our architecture, which demonstrates consistent performance improvements with publicly available benchmarks. Code is publicly available at https://github.com/inhwanbae/GPGraph."
Sequential Multi-View Fusion Network for Fast LiDAR Point Motion Estimation
Authors: Xiaoyan Li, Zhenhua Wang Gang Zhang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The LiDAR point motion estimation, including motion state prediction and velocity estimation, is crucial for understanding a dynamic scene in autonomous driving. Recent 2D projection-based methods run in real-time by applying the well-optimized 2D convolution networks on either the bird’s-eye view (BEV) or the range view (RV) but suffer from lower accuracy due to information loss during the 2D projection. Thus, we propose a novel sequential multi-view fusion network (SMVF), composed of a BEV branch and an RV branch, in charge of encoding the motion information and spatial information, respectively. By looking from distinct views and integrating with the original LiDAR point features, the SMVF produces a comprehensive motion prediction, while keeping its efficiency. Moreover, to generalize the motion estimation well to the objects with fewer training samples, we propose a sequential instance copy-paste (SICP) for generating realistic LiDAR sequences for these objects. The experiments on the SemanticKITTI moving object segmentation (MOS) and Waymo scene flow benchmarks demonstrate that our SMVF outperforms all existing methods by a large margin. \keywords{Motion State Prediction, Velocity Estimation, Multi-View Fusion, Generalization of Motion Estimation}"
E-Graph: Minimal Solution for Rigid Rotation with Extensibility Graphs
Authors: Federico Tombari Yanyan Li
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Minimal solutions for relative rotation and translation estimation tasks have been explored in different scenarios, typically relying on the so-called co-visibility graph. However, how to build direct rotation relationships between two frames without overlap is still an open topic, which, if solved, could greatly improve the accuracy of visual odometry. In this paper, a new minimal solution is proposed to solve relative rotation estimation between two images without overlapping areas by exploiting a new graph structure, which we call Extensibility Graph (E-Graph). Differently from a co-visibility graph, high-level landmarks, including vanishing directions and plane normals, are stored in our E-Graph, which are geometrically extensible. Based on E-Graph, the rotation estimation problem becomes simpler and more elegant, as it can deal with pure rotational motion and requires fewer assumptions, e.g. Manhattan/Atlanta World, planar/vertical motion. Finally, we embed our rotation estimation strategy into a complete camera tracking and mapping system which obtains 6-DoF camera poses and a dense 3D mesh model. Extensive experiments on public benchmarks demonstrate that the proposed method achieves state-of-the-art tracking performance."
Point Cloud Compression with Range Image-Based Entropy Model for Autonomous Driving
Authors: Ming Liu Sukai Wang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "For autonomous driving systems, the storage cost and transmission speed of the large-scale point clouds become an important bottleneck because of their large volume. In this paper, we propose a range image-based three-stage framework to compress the scanning LiDAR’s point clouds using the entropy model. In our three-stage framework, we refine the coarser range image by converting the regression problem into the limited classification problem to improve the performance of generating accurate point clouds. And in the feature extraction part, we propose a novel attention Conv layer to fuse the voxel-based 3D features in the 2D range image. Compared with the Octree-based compression methods, the range image compression with the entropy model performs better in the autonomous driving scene. Experiments on LiDARs with different lines and in different scenarios show that our proposed compression scheme outperforms the state-of-the-art approaches in reconstruction quality and downstream tasks by a wide margin."
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
Authors: Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen Botao Ye
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability. To tackle the above issue, we propose a novel one-stream tracking (OSTrack) framework that unifies feature learning and relation modeling by bridging the template-search image pairs with bidirectional information flows. In this way, discriminative target-oriented features can be dynamically extracted by mutual guidance. Since no extra heavy relation modeling module is needed and the implementation is highly parallelized, the proposed tracker runs at a fast speed. To further improve the inference efficiency, an in-network candidate early elimination module is proposed based on the strong similarity prior calculated in the one-stream framework. As a unified framework, OSTrack achieves state-of-the-art performance on multiple benchmarks, in particular, it shows impressive results on the one-shot tracking benchmark GOT-10k, i.e., achieving 73.7% AO, improving the existing best result (SwinTrack) by 4.3%. Besides, our method maintains a good performance-speed trade-off and shows faster convergence. The code and models are available at https://github.com/botaoye/OSTrack."
MotionCLIP: Exposing Human Motion Generation to CLIP Space
Authors: Brian Gordon, Amir Hertz, Amit H. Bermano, Daniel Cohen-Or Guy Tevet
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding that is disentangled, well behaved, and supports highly semantic textual descriptions. MotionCLIP gains its unique power by aligning its latent space with that of the Contrastive Language-Image Pre-training (CLIP) model. Aligning the human motion manifold to CLIP space implicitly infuses the extremely rich semantic knowledge of CLIP into the manifold. In particular, it helps continuity by placing semantically similar motions close to one another, and disentanglement, which is inherited from the CLIP-space structure. MotionCLIP comprises a transformer-based motion auto-encoder, trained to reconstruct motion while being aligned to its text label’s position in CLIP-space. We further leverage CLIP’s unique visual understanding and inject an even stronger signal through aligning motion to rendered frames in a self-supervised manner. We show that although CLIP has never seen the motion domain, MotionCLIP offers unprecedented text-to-motion abilities, allowing out-of-domain actions, disentangled editing, and abstract language specification. For example, the text prompt “couch” is decoded into a sitting down motion, due to lingual similarity, and the prompt “Spiderman” results in a web-swinging-like solution that is far from seen during training. In addition, we show how the introduced latent space can be leveraged for motion interpolation, editing and recognition."
Backbone Is All Your Need: A Simplified Architecture for Visual Object Tracking
Authors: Peixia Li, Lei Bai, Lei Qiao, Qiuhong Shen, Bo Li, Weihao Gan, Wei Wu, Wanli Ouyang Boyu Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the development of tracking in a more general system. This paper presents a Simplified Tracking architecture (SimTrack) by leveraging a transformer backbone for joint feature extraction and interaction. Unlike existing Siamese trackers, we serialize the input images and concatenate them directly before the one-branch backbone. Feature interaction in the backbone helps to remove well-designed interaction modules and produce a more efficient and effective framework. To reduce the information loss from down-sampling in vision transformers, we further propose a foveal window strategy, providing more diverse input patches with acceptable computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC gains on LaSOT/TNL2K and gets results competitive with other specialized tracking algorithms without bells and whistles. The source codes are available at https://github.com/LPXTT/SimTrack."
Aware of the History: Trajectory Forecasting with the Local Behavior Data
Authors: Zhenyang Ni, Siheng Chen, Ulrich Neumann Yiqi Zhong
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in trajectory forecasting with the guidance of high-definition maps, only a few works have explored such local historical information. In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories. Local behavior data helps the systems emphasize the prediction locality and better understand the impact of static map objects on moving agents. We propose a novel local-behavior-aware (LBA) prediction framework that improves forecasting accuracy by fusing information from observed trajectories, HD maps, and local behavior data. Also, where such historical data is insufficient or unavailable, we employ a local-behavior-free (LBF) prediction framework, which adopts a knowledge-distillation-based architecture to infer the impact of missing data. Extensive experiments demonstrate that upgrading existing methods with these two frameworks significantly improves their performances. Especially, the LBA framework boosts the SOTA methods’ performance on the nuScenes dataset by at least 14% for the K=1 metrics."
Optical Flow Training under Limited Label Budget via Active Learning
Authors: Xian Sun, Hannah Kim, Shuzhi Yu, Carlo Tomasi Shuai Yuan
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Supervised training of optical flow predictors generally yields better accuracy than unsupervised training. However, the improved performance comes at an often high annotation cost. Semi-supervised training trades off accuracy against annotation cost. We use a simple yet effective semi-supervised training method to show that even a small fraction of labels can improve flow accuracy by a significant margin over unsupervised training. In addition, we propose active learning methods based on simple heuristics to further reduce the number of labels required to achieve the same target accuracy. Our experiments on both synthetic and real optical flow datasets show that our semi-supervised networks generally need around 50% of the labels to achieve close to full-label accuracy, and only around 20% with active learning on Sintel. We also analyze and show insights on the factors that may influence active learning performance. Code is available at https://github.com/duke-vision/optical-flow-active-learning-release."
Hierarchical Feature Embedding for Visual Tracking
Authors: Weitao Wan, Chong Sun, Changxin Gao, Nong Sang, Chen Li Zhixiong Pi
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Features extracted by existing tracking methods may contain instance- and category-level information. However, it usually occurs that either instance- or category-level information uncontrollably dominates the feature embeddings depending on the training data distribution, since the two types of information are not explicitly modeled. A more favorable way is to produce features that emphasize both types of information in visual tracking. To achieve this, we propose a hierarchical feature embedding model which separately learns the instance and category information, and progressively embeds them. We develop the instance-aware and category-aware modules that collaborate from different semantic levels to produce discriminative and robust feature embeddings. The instance-aware module concentrates on the instance level in which the inter-video contrastive learning mechanism is adopted to facilitate inter-instance separability and intra-instance compactness. However, it is challenging to force the intra-instance compactness by using instance-level information alone because of the prevailing appearance changes of the instance in visual tracking. To tackle this problem, the category-aware module is employed to summarize high-level category information which remains robust despite instance-level appearance changes. As such, intra-instance compactness can be effectively improved by jointly leveraging the instance- and category-aware modules. Experimental results on various tracking benchmarks demonstrate that the proposed method performs favorably against the state-of-the-arts."
Tackling Background Distraction in Video Object Segmentation
Authors: Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, Sangyoun Lee Suhwan Cho
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background distractors that appear similar to the target objects. We propose three novel strategies to suppress such distractors: 1) a spatio-temporally diversified template construction scheme to obtain generalized properties of the target objects; 2) a learnable distance-scoring function to exclude spatially-distant distractors by exploiting the temporal consistency between two consecutive frames; 3) swap-and-attach augmentation to force each object to have unique features by providing training samples containing entangled objects. On all public benchmark datasets, our model achieves a comparable performance to contemporary state-of-the-art approaches, even with real-time performance. Qualitative results also demonstrate the superiority of our approach over existing methods. We believe our approach will be widely used for future VOS research."
Social-Implicit: Rethinking Trajectory Prediction Evaluation and the Effectiveness of Implicit Maximum Likelihood Estimation
Authors: Deyao Zhu, Warren Vu, Mohamed Elhoseiny, Christian Claudel Abduallah Mohamed
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Best-of-N (BoN) Average Displacement Error (ADE)/ Final Displacement Error (FDE) is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not quantify the whole generated samples, resulting in an incomplete view of the model’s prediction quality and performance. We propose a new metric, Average Mahalanobis Distance (AMD) to tackle this issue. AMD is a metric that quantifies how close the whole generated samples are to the ground truth. We also introduce the Average Maximum Eigenvalue (AMV) metric that quantifies the overall spread of the predictions. Our metrics are validated empirically by showing that the ADE/FDE is not sensitive to distribution shifts, giving a biased sense of accuracy, unlike the AMD/AMV metrics. We introduce the usage of Implicit Maximum Likelihood Estimation (IMLE) as a replacement for traditional generative models to train our model, Social-Implicit. IMLE training mechanism aligns with AMD/AMV objective of predicting trajectories that are close to the ground truth with a tight spread. Social-Implicit is a memory efficient deep model with only 5.8K parameters that runs in real time of about 580Hz and achieves competitive results."
TEMOS: Generating Diverse Human Motions from Textual Descriptions
Authors: Michael J. Black, Gül Varol Mathis Petrovich
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We address the problem of generating diverse 3D human motions from textual descriptions. This challenging task requires joint modeling of both modalities: understanding and extracting useful human-centric information from the text, and then generating plausible and realistic sequences of human poses. In contrast to most previous work which focuses on generating a single, deterministic, motion from a textual description, we design a variational approach that can produce multiple diverse human motions. We propose TEMOS, a text-conditioned generative model leveraging variational autoencoder (VAE) training with human motion data, in combination with a text encoder that produces distribution parameters compatible with the VAE latent space. We show the TEMOS framework can produce both skeleton-based animations as in prior work, as well more expressive SMPL body motions. We evaluate our approach on the KIT Motion-Language benchmark and, despite being relatively straightforward, demonstrate significant improvements over the state of the art. Code and models are available on our webpage."
Tracking Every Thing in the Wild
Authors: Martin Danelljan, Henghui Ding, Thomas E. Huang, Fisher Yu Siyuan Li
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Current multi-category Multiple Object Tracking (MOT) metrics use class labels to group tracking results for per-class evaluation. Similarly, MOT methods typically only associate objects with the same class predictions. These two prevalent strategies in MOT implicitly assume that the classification performance is near-perfect. However, this is far from the case in recent large-scale MOT datasets, which contain large numbers of classes with many rare or semantically similar categories. Therefore, the resulting inaccurate classification leads to sub-optimal tracking and inadequate benchmarking of trackers. We address these issues by disentangling classification from tracking. We introduce a new metric, Track Every Thing Accuracy (TETA), breaking tracking measurement into three sub-factors: localization, association, and classification, allowing comprehensive benchmarking of tracking performance even under inaccurate classification. TETA also deals with the challenging incomplete annotation problem in large-scale tracking datasets. We further introduce a Track Every Thing tracker (TETer), that performs association using Class Exemplar Matching (CEM). Our experiments show that TETA evaluates trackers more comprehensively, and TETer achieves significant improvements on the challenging large-scale datasets BDD100K and TAO compared to the state-of-the-art."
HULC: 3D HUman Motion Capture with Pose Manifold SampLing and Dense Contact Guidance
Authors: Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, Christian Theobalt Soshi Shimada
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Marker-less monocular 3D human motion capture (MoCap) with scene interactions is a challenging research topic relevant for extended reality, robotics and virtual avatar generation. Due to the inherent depth ambiguity of monocular settings, 3D motions captured with existing methods often contain severe artefacts such as incorrect body-scene inter-penetrations, jitter and body floating. To tackle these issues, we propose HULC, a new approach for 3D human MoCap which is aware of the scene geometry. HULC estimates 3D poses and dense body-environment surface contacts for improved 3D localisations, as well as the absolute scale of the subject. Furthermore, we introduce a 3D pose trajectory optimisation based on a novel pose manifold sampling that resolves erroneous body-environment inter-penetrations. Although the proposed method requires less structured inputs compared to existing scene-aware monocular MoCap algorithms, it produces more physically plausible poses: HULC significantly and consistently outperforms the existing approaches in various experiments and on different metrics. Project page: https://vcai.mpi-inf.mpg.de/projects/HULC/"
Towards Sequence-Level Training for Visual Tracking
Authors: Seungkwan Lee, Jungseul Ok, Bohyung Han, Minsu Cho Minji Kim
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives. This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning and discusses how a sequence-level design of data sampling, learning objectives, and data augmentation can improve the accuracy and robustness of tracking algorithms. Our experiments on standard benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training without modifying architectures."
Learned Monocular Depth Priors in Visual-Inertial Initialization
Authors: Abhishek Kar, Eric Turner, Adarsh Kowdle, Chao X. Guo, Ryan C. DuToit, Konstantine Tsotsos Yunwen Zhou
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR and autonomous robotic systems today, in both academia and industry. However, these systems are highly sensitive to the initialization of key parameters such as sensor biases, gravity direction, and metric scale. In practical scenarios where high-parallax or variable acceleration assumptions are rarely met (e.g. hovering aerial robot, smartphone AR user not gesticulating with phone), classical visual-inertial initialization formulations often become ill-conditioned and/or fail to meaningfully converge. In this paper we target visual-inertial initialization specifically for these low-excitation scenarios critical to in-the-wild usage. We propose to circumvent the limitations of classical visual-inertial structure-from-motion (SfM) initialization by incorporating a new learning-based measurement as a higher-level input. We leverage learned monocular depth images (mono-depth) to constrain the relative depth of features, and upgrade the mono-depths to metric scale by jointly optimizing for their scales and shifts. Our experiments show a significant improvement in problem conditioning compared to a classical formulation for visual-inertial initialization, and demonstrate significant accuracy and robustness improvements relative to the state-of-the-art on public benchmarks, particularly under low-excitation scenarios. We further extend this improvement to implementation within an existing odometry system to illustrate the impact of our improved initialization method on resulting tracking trajectories."
Robust Visual Tracking by Segmentation
Authors: Martin Danelljan, Christoph Mayer, Luc Van Gool Matthieu Paul
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Estimating the target extent poses a fundamental challenge in visual object tracking. Typically, trackers are box-centric and fully rely on a bounding box to define the target in the scene. In practice, objects often have complex shapes and are not aligned with the image axis. In these cases, bounding boxes do not provide an accurate description of the target and often contain a majority of background pixels. We propose a segmentation-centric tracking pipeline that not only produces a highly accurate segmentation mask, but also internally works with segmentation masks instead of bounding boxes. Thus, our tracker is able to better learn a target representation that clearly differentiates the target in the scene from background content. In order to achieve the necessary robustness for the challenging tracking scenario, we propose a separate instance localization component that is used to condition the segmentation decoder when producing the output mask. We infer a bounding box from the segmentation mask, validate our tracker on challenging tracking datasets and achieve the new state of the art on LaSOT with a success AUC score of 69.7%. Since most tracking datasets do not contain mask annotations, we cannot use them to evaluate predicted segmentation masks. Instead, we validate our segmentation quality on two popular video object segmentation datasets. The code and trained models are available at https://github.com/visionml/pytracking."
MeshLoc: Mesh-Based Visual Localization
Authors: Zuzana Kukelova, Torsten Sattler Vojtech Panek
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Visual localization, i.e., the problem of camera pose estimation, is a central component of applications such as autonomous robots and augmented reality systems. A dominant approach in the literature, shown to scale to large scenes and to handle complex illumination and seasonal changes, is based on local features extracted from images. The scene representation is a sparse Structure-from-Motion point cloud that is tied to a specific local feature. Switching to another feature type requires an expensive feature matching step between the database images used to construct the point cloud. In this work, we thus explore a more flexible alternative based on dense 3D meshes that does not require features matching between database images to build the scene representation. We show that this approach can achieve state-of-the-art results. We further show that surprisingly competitive results can be obtained when extracting features on renderings of these meshes, without any neural rendering stage, and even when rendering raw scene geometry without color or texture. Our results show that dense 3D model-based representations are a promising alternative to existing representations and point to interesting and challenging directions for future research."
S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction
Authors: Hsuan-Kung Yang, Chu-Chi Chiu, Chun-Yi Lee Yu-Wen Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. The architecture of S2F2 consists of two primary parts: (1) a context feature extractor responsible for extracting a shared latent feature embedding for performing detection and Re-ID, and (2) a forecasting module responsible for extracting a shared latent feature embedding for forecasting. The outputs of the two parts are then processed to generate the final predicted trajectories of pedestrians. Unlike previous approaches, the computational burden of S2F2 remains consistent even if the number of pedestrians grows. In order to fairly compare S2F2 against the other approaches, we designed a StaticMOT dataset that excludes video sequences involving egocentric motions. The experimental results demonstrate that S2F2 is able to outperform two conventional trajectory forecasting algorithms and a recent learning-based two-stage model, while maintaining tracking performance on par with the contemporary MOT models."
Large-Displacement 3D Object Tracking with Hybrid Non-local Optimization
Authors: Xinran Lin, Fan Zhong, Xueying Qin Xuhui Tian
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements due to the local minimum, which usually requires large amount of computation to be overcome. In this paper we propose a fast and effective non-local 3D tracking method. Based on the observation that local minimum are mostly due to the out-of-plane rotation, we propose a hybrid approach combining non-local and local optimizations for different parameters, resulting in efficient non-local search in the 6D pose space. In addition, a precomputed robust contour-based tracking method is proposed for the local optimization. By using long search lines with multiple candidate correspondences, it can better adaptive to different frame displacements without the need of coarse-to-fine search. After the pre-computation, pose updates can be conducted very fast, enabling the non-local optimization in real time. Our method outperforms all previous methods for both small and large displacements. For large displacements, the accuracy is greatly improved (81.7% v.s.19.4%). At the same time, real-time speed (>50fps) can be achieved with only CPU.The source code is available at https://github.com/cvbubbles/nonlocal-3dtracking."
"FEAR: Fast, Efficient, Accurate and Robust Visual Tracker"
Authors: Roman Vei, Orest Kupyn, Tetiana Martyniuk, Igor Krashenyi, Jiři Matas Vasyl Borsuk
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for object model adaption, which incorporates temporal information with only a single learnable parameter. We further improve the tracker architecture with a pixel-wise fusion block. By plugging-in sophisticated backbones with the abovementioned modules, FEAR-M and FEAR-L trackers surpass most Siamese trackers on several academic benchmarks in both accuracy and efficiency. Employed with the lightweight backbone, the optimized version FEAR-XS offers more than 10 times faster tracking than current Siamese trackers while maintaining near state-of-the-art results. FEAR-XS tracker is 2.4x smaller and 4.3x faster than LightTrack with superior accuracy. In addition, we expand the definition of the model efficiency by introducing FEAR benchmark that assesses energy consumption and execution speed. We show that energy consumption is a limiting factor for trackers on mobile devices. Source code, pretrained models, and evaluation protocol are available at https://github.com/PinataFarms/FEARTracker."
PREF: Predictability Regularized Neural Motion Fields
Authors: Xuan Gong, Benjamin Planche, Meng Zheng, David Doermann, Junsong Yuan, Terrence Chen, Ziyan Wu Liangchen Song
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene."