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We introduce a method to synthesize animator guided human motion across 3Dscenes. Given a set of sparse (3 or 4) joint locations (such as the location ofa person's hand and two feet) and a seed motion sequence in a 3D scene, ourmethod generates a plausible motion sequence starting from the seed motionwhile satisfying the constraints imposed by the provided keypoints. Wedecompose the continual motion synthesis problem into walking along paths andtransitioning in and out of the actions specified by the keypoints, whichenables long generation of motions that satisfy scene constraints withoutexplicitly incorporating scene information. Our method is trained only usingscene agnostic mocap data. As a result, our approach is deployable across 3Dscenes with various geometries. For achieving plausible continual motionsynthesis without drift, our key contribution is to generate motion in agoal-centric canonical coordinate frame where the next immediate target issituated at the origin. Our model can generate long sequences of diverseactions such as grabbing, sitting and leaning chained together in arbitraryorder, demonstrated on scenes of varying geometry: HPS, Replica, Matterport,ScanNet and scenes represented using NeRFs. Several experiments demonstratethat our method outperforms existing methods that navigate paths in 3D scenes.

Generating Continual Human Motion in Diverse 3D Scenes