DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding

TL;DR

DisCoRD introduces a novel rectified flow decoding method that converts discrete motion tokens into smooth, natural continuous human motions, effectively bridging the gap between discrete efficiency and continuous realism in motion generation.

Contribution

The paper presents a new rectified flow-based decoding approach that enhances discrete motion tokens into realistic continuous motions, improving naturalness and fidelity in human motion synthesis.

Findings

Achieves state-of-the-art FID scores on HumanML3D and KIT-ML datasets.

Enhances motion naturalness without losing conditioning fidelity.

Compatible with any discrete-based motion generation framework.

Abstract

Human motion is inherently continuous and dynamic, posing significant challenges for generative models. While discrete generation methods are widely used, they suffer from limited expressiveness and frame-wise noise artifacts. In contrast, continuous approaches produce smoother, more natural motion but often struggle to adhere to conditioning signals due to high-dimensional complexity and limited training data. To resolve this 'discord' between discrete and continuous representations we introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding, a novel method that leverages rectified flow to decode discrete motion tokens in the continuous, raw motion space. Our core idea is to frame token decoding as a conditional generation task, ensuring that DisCoRD captures fine-grained dynamics and achieves smoother, more natural motions. Compatible with any discrete-based framework, our method enhances naturalness without compromising faithfulness to the conditioning signals on diverse settings. Extensive evaluations demonstrate that DisCoRD achieves state-of-the-art performance, with FID of 0.032 on HumanML3D and 0.169 on KIT-ML. These results establish DisCoRD as a robust solution for bridging the divide between discrete efficiency and continuous realism. Project website: https://whwjdqls.github.io/discord-motion/