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This paper presents a method to reconstruct dense semantic trajectory streamof human interactions in 3D from synchronized multiple videos. The interactionsinherently introduce self-occlusion and illumination/appearance/shape changes,resulting in highly fragmented trajectory reconstruction with noisy and coarsesemantic labels. Our conjecture is that among many views, there exists a set ofviews that can confidently recognize the visual semantic label of a 3Dtrajectory. We introduce a new representation called 3D semantic map---aprobability distribution over the semantic labels per trajectory. We constructthe 3D semantic map by reasoning about visibility and 2D recognition confidencebased on view-pooling, i.e., finding the view that best represents thesemantics of the trajectory. Using the 3D semantic map, we precisely infer alltrajectory labels jointly by considering the affinity between long rangetrajectories via estimating their local rigid transformations. This inferencequantitatively outperforms the baseline approaches in terms of predictivevalidity, representation robustness, and affinity effectiveness. We demonstratethat our algorithm can robustly compute the semantic labels of a large scaletrajectory set involving real-world human interactions with object, scenes, andpeople.

3D Semantic Trajectory Reconstruction from 3D Pixel Continuum