DisCoRD：通過矯正流解碼從離散標記到連續運動

DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding

November 29, 2024

作者: Jungbin Cho, Junwan Kim, Jisoo Kim, Minseo Kim, Mingu Kang, Sungeun Hong, Tae-Hyun Oh, Youngjae Yu

cs.AI

摘要

人類運動本質上是連續且動態的，對生成模型提出了重大挑戰。儘管離散量化方法（如VQ-VAEs）佔主導地位，但存在固有限制，包括表現受限和逐幀噪音。連續方法雖然產生更平滑和更自然的動作，但由於高維度複雜性和有限的訓練數據，通常會遇到困難。為了解決離散和連續表示之間的“不協調”，我們引入了DisCoRD：通過矯正流解碼將離散運動令牌解碼為連續運動的新方法。通過在連續空間中採用迭代細化過程，DisCoRD捕捉了細粒度動態並確保更平滑和更自然的運動。我們的方法與任何基於離散的框架兼容，增強了自然性，同時不損害對條件信號的忠實度。廣泛的評估表明，DisCoRD在HumanML3D和KIT-ML上的FID分別為0.032和0.169，實現了最先進的性能。這些結果鞏固了DisCoRD作為彌合離散效率和連續現實主義之間差距的堅固解決方案。我們的項目頁面位於：https://whwjdqls.github.io/discord.github.io/。

English

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/.