DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow
  Decoding
Authors: name: Jungbin Cho, name: Junwan Kim, name: Jisoo Kim, name: Minseo Kim, name: Mingu Kang, name: Sungeun Hong, name: Tae-Hyun Oh, name: Youngjae Yu

Human motion, inherently continuous and dynamic, presents significant
challenges for generative models. Despite their dominance, discrete
quantization methods, such as VQ-VAEs, suffer from inherent limitations,
including restricted expressiveness and frame-wise noise artifacts. Continuous
approaches, while producing smoother and more natural motions, often falter 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 decodes discrete motion tokens into continuous motion through
rectified flow. By employing an iterative refinement process in the continuous
space, DisCoRD captures fine-grained dynamics and ensures smoother and more
natural motions. Compatible with any discrete-based framework, our method
enhances naturalness without compromising faithfulness to the conditioning
signals. 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 solidify DisCoRD as a robust solution for bridging the
divide between discrete efficiency and continuous realism. Our project page is
available at: https://whwjdqls.github.io/discord.github.io/.