DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow
  Decoding
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/.
674d17baaa794726fac6a6c9
Jungbin Cho
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2024-12-02T08:49:56.163Z
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jbc
user
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Junwan Kim
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2024-12-02T08:49:53.178Z
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Jun-Wan KIM
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junwann
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Jisoo Kim
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Minseo Kim
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2024-12-02T12:52:35.160Z
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Kim Minseo
user
min99830
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Mingu Kang
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2024-12-03T14:40:42.003Z
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kangmingu
user
rkdrn79
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Sungeun Hong
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Tae-Hyun Oh
claimed_verified
2024-12-02T17:31:58.388Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/0ga3pvGwd8oTEEWxIuPr6.png
Tae-Hyun Oh
user
taehyunoh
674d17baaa794726fac6a6d0
Youngjae Yu
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow
  Decoding
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/.
DisCoRD enhances human motion generation by translating discrete motion tokens into continuous motion through rectified flow, achieving state-of-the-art performance.
VQ-VAEs DisCoRD rectified flow continuous motion iterative refinement HumanML3D KIT-ML FID