Disentangled Motion Representation: Encoding Full-Body Avatars into Discrete Latent Spaces | HackerNoon

Table of Links

Abstract and 1. Introduction

2. Related Work

   2.1. Motion Reconstruction from Sparse Input

   2.2. Human Motion Generation

3. SAGE: Stratified Avatar Generation and 3.1. Problem Statement and Notation

   3.2. Disentangled Motion Representation

   3.3. Stratified Motion Diffusion

   3.4. Implementation Details

4. Experiments and Evaluation Metrics

   4.1. Dataset and Evaluation Metrics

   4.2. Quantitative and Qualitative Results

   4.3. Ablation Study

5. Conclusion and References

Supplementary Material

A. Extra Ablation Studies

B. Implementation Details

3.2. Disentangled Motion Representation

In this section, our objective is to disentangle full-body human motions into upper-body and lower-body parts and encode them to discrete latent spaces. This can effectively reduce the complexity and burden of encoding since each encoding takes care of only half-body motions.

Since continuous latent from all data samples share the same codebook C, all the real motions in the training set could be expressed by a finite number of bases in latent space.

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Authors:

(1) Han Feng, equal contributions, ordered by alphabet from Wuhan University;

(2) Wenchao Ma, equal contributions, ordered by alphabet from Pennsylvania State University;

(3) Quankai Gao, University of Southern California;

(4) Xianwei Zheng, Wuhan University;

(5) Nan Xue, Ant Group ([email protected]);

(6) Huijuan Xu, Pennsylvania State University.

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This paper is available on arxiv under CC BY 4.0 DEED license.

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