motion
Papers with tag motion
2022
- Multi-view Tracking Using Weakly Supervised Human Motion PredictionMartin Engilberge, Weizhe Liu, and Pascal FuaIn 2022
Multi-view approaches to people-tracking have the potential to better handleocclusions than single-view ones in crowded scenes. They often rely on thetracking-by-detection paradigm, which involves detecting people first and thenconnecting the detections. In this paper, we argue that an even more effectiveapproach is to predict people motion over time and infer people’s presence inindividual frames from these. This enables to enforce consistency both overtime and across views of a single temporal frame. We validate our approach onthe PETS2009 and WILDTRACK datasets and demonstrate that it outperformsstate-of-the-art methods.
multi-view tracking motion@inproceedings{2210.10771, title = {Multi-view Tracking Using Weakly Supervised Human Motion Prediction}, author = {Engilberge, Martin and Liu, Weizhe and Fua, Pascal}, year = {2022}, tags = {multi-view, tracking, motion}, } - Contact-aware Human Motion ForecastingWei Mao, Miaomiao Liu, Richard Hartley, and Mathieu SalzmannIn 2022
In this paper, we tackle the task of scene-aware 3D human motion forecasting,which consists of predicting future human poses given a 3D scene and a pasthuman motion. A key challenge of this task is to ensure consistency between thehuman and the scene, accounting for human-scene interactions. Previous attemptsto do so model such interactions only implicitly, and thus tend to produceartifacts such as "ghost motion" because of the lack of explicit constraintsbetween the local poses and the global motion. Here, by contrast, we propose toexplicitly model the human-scene contacts. To this end, we introducedistance-based contact maps that capture the contact relationships betweenevery joint and every 3D scene point at each time instant. We then develop atwo-stage pipeline that first predicts the future contact maps from the pastones and the scene point cloud, and then forecasts the future human poses byconditioning them on the predicted contact maps. During training, we explicitlyencourage consistency between the global motion and the local poses via a priordefined using the contact maps and future poses. Our approach outperforms thestate-of-the-art human motion forecasting and human synthesis methods on bothsynthetic and real datasets. Our code is available athttps://github.com/wei-mao-2019/ContAwareMotionPred.
motion-prediction contact@inproceedings{2210.03954, title = {Contact-aware Human Motion Forecasting}, author = {Mao, Wei and Liu, Miaomiao and Hartley, Richard and Salzmann, Mathieu}, year = {2022}, tags = {motion-prediction, contact}, }
- Learning Visual Locomotion with Cross-Modal SupervisionAntonio Loquercio, Ashish Kumar, and Jitendra MalikIn 2022
In this work, we show how to learn a visual walking policy that only uses amonocular RGB camera and proprioception. Since simulating RGB is hard, wenecessarily have to learn vision in the real world. We start with a blindwalking policy trained in simulation. This policy can traverse some terrains inthe real world but often struggles since it lacks knowledge of the upcominggeometry. This can be resolved with the use of vision. We train a visual modulein the real world to predict the upcoming terrain with our proposed algorithmCross-Modal Supervision (CMS). CMS uses time-shifted proprioception tosupervise vision and allows the policy to continually improve with morereal-world experience. We evaluate our vision-based walking policy over adiverse set of terrains including stairs (up to 19cm high), slippery slopes(inclination of 35 degrees), curbs and tall steps (up to 20cm), and complexdiscrete terrains. We achieve this performance with less than 30 minutes ofreal-world data. Finally, we show that our policy can adapt to shifts in thevisual field with a limited amount of real-world experience. Video results andcode at https://antonilo.github.io/vision_locomotion/.
在walking robotics中加入了visual module, 学习处理upcoming geometry. 通过proprioception在real world中训练这个visual module.
visual-locomotion@inproceedings{Learning_Visual_Locomotion, title = {Learning Visual Locomotion with Cross-Modal Supervision}, author = {Loquercio, Antonio and Kumar, Ashish and Malik, Jitendra}, year = {2022}, tags = {visual-locomotion}, sida = {在walking robotics中加入了visual module, 学习处理upcoming geometry. 通过proprioception在real world中训练这个visual module.}, }