multi-view
Papers with tag multi-view
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}, } - Multi-view Human Body Mesh TranslatorXiangjian Jiang, Xuecheng Nie, Zitian Wang, Luoqi Liu, and Si LiuIn 2022
Existing methods for human mesh recovery mainly focus on single-viewframeworks, but they often fail to produce accurate results due to theill-posed setup. Considering the maturity of the multi-view motion capturesystem, in this paper, we propose to solve the prior ill-posed problem byleveraging multiple images from different views, thus significantly enhancingthe quality of recovered meshes. In particular, we present a novel\textbfMulti-view human body \textbfMesh \textbfTranslator (MMT) modelfor estimating human body mesh with the help of vision transformer.Specifically, MMT takes multi-view images as input and translates them totargeted meshes in a single-forward manner. MMT fuses features of differentviews in both encoding and decoding phases, leading to representations embeddedwith global information. Additionally, to ensure the tokens are intensivelyfocused on the human pose and shape, MMT conducts cross-view alignment at thefeature level by projecting 3D keypoint positions to each view and enforcingtheir consistency in geometry constraints. Comprehensive experimentsdemonstrate that MMT outperforms existing single or multi-view models by alarge margin for human mesh recovery task, notably, 28.8% improvement in MPVEover the current state-of-the-art method on the challenging HUMBI dataset.Qualitative evaluation also verifies the effectiveness of MMT in reconstructinghigh-quality human mesh. Codes will be made available upon acceptance.
multi-view SMPL transformer@inproceedings{2210.01886, title = {Multi-view Human Body Mesh Translator}, author = {Jiang, Xiangjian and Nie, Xuecheng and Wang, Zitian and Liu, Luoqi and Liu, Si}, year = {2022}, tags = {multi-view, SMPL, transformer}, }