title: DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow
    Decoding
summary: 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/.
paper_authors._id: 674d17baaa794726fac6a6c9
paper_authors.name: Jungbin Cho
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T08:49:56.163Z
paper_authors._id: 65b5d43419d2172f003fed47
paper_authors.avatarUrl: /avatars/3683356c1da06ddfe93fa674aa4481f0.svg
paper_authors.fullname: jbc
paper_authors.type: user
paper_authors.user: whwjdqls99
paper_authors._id: 674d17baaa794726fac6a6ca
paper_authors.name: Junwan Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T08:49:53.178Z
paper_authors._id: 64c2c45ae818eec6128fdda3
paper_authors.avatarUrl: /avatars/d4399e25e6399345e263c7902789047e.svg
paper_authors.fullname: Jun-Wan KIM
paper_authors.type: user
paper_authors.user: junwann
paper_authors._id: 674d17baaa794726fac6a6cb
paper_authors.name: Jisoo Kim
paper_authors._id: 674d17baaa794726fac6a6cc
paper_authors.name: Minseo Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T12:52:35.160Z
paper_authors._id: 64f22ee8a2fc808156dd1872
paper_authors.avatarUrl: /avatars/45d9140acbfdbb0a8b044c2f9d3c4fad.svg
paper_authors.fullname: Kim Minseo
paper_authors.type: user
paper_authors.user: min99830
paper_authors._id: 674d17baaa794726fac6a6cd
paper_authors.name: Mingu Kang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-03T14:40:42.003Z
paper_authors._id: 66388b9b7517eefed69a13da
paper_authors.avatarUrl: /avatars/bbac095ab6e6c1df78b9a9d630a6c207.svg
paper_authors.fullname: kangmingu
paper_authors.type: user
paper_authors.user: rkdrn79
paper_authors._id: 674d17baaa794726fac6a6ce
paper_authors.name: Sungeun Hong
paper_authors._id: 674d17baaa794726fac6a6cf
paper_authors.name: Tae-Hyun Oh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T17:31:58.388Z
paper_authors._id: 674622d01310ed05c6c5a5aa
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/0ga3pvGwd8oTEEWxIuPr6.png
paper_authors.fullname: Tae-Hyun Oh
paper_authors.type: user
paper_authors.user: taehyunoh
paper_authors._id: 674d17baaa794726fac6a6d0
paper_authors.name: Youngjae Yu
paper_title: DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow
    Decoding
paper_summary: 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/.
paper_ai_keywords: VQ-VAEs DisCoRD Discrete Tokens to Continuous Motion via Rectified Flow Decoding rectified flow continuous space fine-grained dynamics HumanML3D KIT-ML FID