Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder

TL;DR

This paper introduces a CVAE-based deep generative model that produces diverse, plausible human motion interpolations conditioned on start and end motions, leveraging RNNs and a regularization loss to enhance sample diversity.

Contribution

The paper proposes a novel CVAE framework with a regularization loss for diverse human motion interpolation, improving upon existing methods by generating multiple plausible motions.

Findings

Effective in generating diverse and plausible motions

Regularization enhances sample diversity

Validated on public dataset with positive results

Abstract

In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the Conditional Variational Auto-Encoder (CVAE) to learn human motion conditioned on a pair of given start and end motions, by leveraging the Recurrent Neural Network (RNN) structure for both the encoder and the decoder. Additionally, we introduce a regularization loss to further promote sample diversity. Once trained, our method is able to generate multiple plausible coherent motions by repetitively sampling from the learned latent space. Experiments on the publicly available dataset demonstrate the effectiveness of our method, in terms of sample plausibility and diversity.