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We present a unified framework for understanding 3D hand and objectinteractions in raw image sequences from egocentric RGB cameras. Given a singleRGB image, our model jointly estimates the 3D hand and object poses, modelstheir interactions, and recognizes the object and action classes with a singlefeed-forward pass through a neural network. We propose a single architecturethat does not rely on external detection algorithms but rather is trainedend-to-end on single images. We further merge and propagate information in thetemporal domain to infer interactions between hand and object trajectories andrecognize actions. The complete model takes as input a sequence of frames andoutputs per-frame 3D hand and object pose predictions along with the estimatesof object and action categories for the entire sequence. We demonstratestate-of-the-art performance of our algorithm even in comparison to theapproaches that work on depth data and ground-truth annotations.

H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions