Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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书目名称Computer Vision – ECCV 2022影响因子(影响力)




书目名称Computer Vision – ECCV 2022影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2022网络公开度




书目名称Computer Vision – ECCV 2022网络公开度学科排名




书目名称Computer Vision – ECCV 2022被引频次




书目名称Computer Vision – ECCV 2022被引频次学科排名




书目名称Computer Vision – ECCV 2022年度引用




书目名称Computer Vision – ECCV 2022年度引用学科排名




书目名称Computer Vision – ECCV 2022读者反馈




书目名称Computer Vision – ECCV 2022读者反馈学科排名

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,Pose Forecasting in Industrial Human-Robot Collaboration,ions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 ms, and collision detection, by comparing the for

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,Domain Knowledge-Informed Self-supervised Representations for Workout Form Assessment,ngles, clothes, and illumination to learn powerful representations. To facilitate our self-supervised pretraining, and supervised finetuning, we curated a new exercise dataset, . (.), comprising of three exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by expert trainers fo

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,Responsive Listening Head Generation: A Benchmark Dataset and Baseline,ation, listening head generation takes as input both the audio and visual signals from the speaker, and gives non-verbal feedbacks (.., head motions, facial expressions) in a real-time manner. Our dataset supports a wide range of applications such as human-to-human interaction, video-to-video transl

Eulogy

,Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integratitainty measure, which is non-trivial for unsupervised methods. By leveraging IMU during training, DynaDepth not only learns an absolute scale, but also provides a better generalization ability and robustness against vision degradation such as illumination change and moving objects. We validate the e

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TIPS: Text-Induced Pose Synthesis,pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.