Figure S1: (a) Illustration of the proposed hierarchical query embedding and the input-dependent query adaptation schemes. It consist of a self-attention, a projective attention and a feed-forward network (FFN) with residual connections. Fig. S1 (a) illustrates our proposed hierarchical query The decoder of MvP transformer consists of multiple decoder layers for regressing 3D joint locations progressively. Fig. S1 (b) demonstrates the detailed architecture of a decoder layer, Results are shown in Table S1. Table S1: Results of replacing camera ray directions with 2D coordinates in RayConv.Positional Input AP We further investigate the effectiveness of the proposed projective attention by comparing it with the dense dot product attention, i.e., conducting Results are given in Table S2.

Notably, it achieves 92.3% AP 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. Additionally, we mitigate the commonly faced generalization issue by a simple query adaptation strategy.

We present Multi-view Pose transformer (MvP) for estimating multi-person 3D poses from multi-view images. Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks. Specifically, MvP represents skeleton joints as learnable query embeddings and let them progressively attend to and reason over the multi-view information from the input images to directly regress the actual 3D joint locations. To improve the accuracy of such a simple pipeline, MvP presents a hierarchical scheme to concisely represent query embeddings of multi-person skeleton joints and introduces an input-dependent query adaptation approach. Further, MvP designs a novel geometrically guided attention mechanism, called projective attention, to more precisely fuse the cross-view information for each joint.