Invention Publication
- Patent Title: GENERATING HUMAN MOTION SEQUENCES UTILIZING UNSUPERVISED LEARNING OF DISCRETIZED FEATURES VIA A NEURAL NETWORK ENCODER-DECODER
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Application No.: US18756135Application Date: 2024-06-27
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Publication No.: US20240346737A1Publication Date: 2024-10-17
- Inventor: Jun Saito , Nitin Saini , Ruben Villegas
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: G06T13/40
- IPC: G06T13/40 ; G06T9/00 ; G06T13/20 ; G06T17/00

Abstract:
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing unsupervised learning of discrete human motions to generate digital human motion sequences. The disclosed system utilizes an encoder of a discretized motion model to extract a sequence of latent feature representations from a human motion sequence in an unlabeled digital scene. The disclosed system also determines sampling probabilities from the sequence of latent feature representations in connection with a codebook of discretized feature representations associated with human motions. The disclosed system converts the sequence of latent feature representations into a sequence of discretized feature representations by sampling from the codebook based on the sampling probabilities. Additionally, the disclosed system utilizes a decoder to reconstruct a human motion sequence from the sequence of discretized feature representations. The disclosed system also utilizes a reconstruction loss and a distribution loss to learn parameters of the discretized motion model.
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