Moreover, they estimate 3D pose for each person separately, making the computation cost grow linearly with the number of persons. Though with notable accuracy, the above paradigms are inefficient due to highly relying on those intermediate tasks. ( 2020) builds a 3D feature volume through heatmap estimation and 2D-to-3D un-projection at first, based on which instance localization and 3D pose estimation are performed for each person instance individually. The former first estimates 2D poses in each view independently and then aggregates them and reconstructs their 3D counterparts via triangulation or a 3D pictorial structure model. ( 2020) approaches in previous literature, as shown in Fig. Is mainly tackled by reconstruction-based Dong et al. It is a fundamental task that benefits many real-world applications (such as surveillance, sportscast, gaming and mixed reality) and Multi-view multi-person 3D pose estimation aims to localize 3D skeleton joints for each person instance in a scene from multi-view camera inputs. Model, thus useful for modeling multi-person body shapes. Is general and also extendable to recovering human mesh represented by the SMPL Panoptic dataset, improving upon the previous best approach by 9.8 Model outperforms the state-of-the-art methods on several benchmarks whileīeing much more efficient. Integrate the view-dependent camera geometry into the feature representationsįor augmenting the projective attention. MvP also introduces a Ra圜onv operation to Mechanism, called projective attention, to more precisely fuse the cross-view Further, MvP designs a novel geometrically guided attention Multi-person skeleton joints and introduces an input-dependent query adaptationĪpproach. Presents a hierarchical scheme to concisely represent query embeddings of To improve the accuracy of such a simple pipeline, MvP Multi-view information from the input images to directly regress the actual 3D Query embeddings and let them progressively attend to and reason over the Specifically, MvP represents skeleton joints as learnable Multi-person 3D poses in a clean and efficient way, without relying on Multiple detected 2D poses as in previous methods, MvP directly regresses the Instead of estimating 3D joint locations fromĬostly volumetric representation or reconstructing the per-person 3D pose from We present Multi-view Pose transformer (MvP) for estimating multi-person 3D