Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer

• URL: http://arxiv.org/abs/2603.19227v1
• Date: Thu, 19 Mar 2026 17:59:51 GMT
• Title: Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer
• Abstract summary: MoTok is a discrete motion tokenizer that decouples semantic abstraction from fine-grained reconstruction.<n>On HumanML3D, our method significantly improves controllability and fidelity over MaskControl while using only one-sixth of the tokens.
• Score: 55.9892973179428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prior motion generation largely follows two paradigms: continuous diffusion models that excel at kinematic control, and discrete token-based generators that are effective for semantic conditioning. To combine their strengths, we propose a three-stage framework comprising condition feature extraction (Perception), discrete token generation (Planning), and diffusion-based motion synthesis (Control). Central to this framework is MoTok, a diffusion-based discrete motion tokenizer that decouples semantic abstraction from fine-grained reconstruction by delegating motion recovery to a diffusion decoder, enabling compact single-layer tokens while preserving motion fidelity. For kinematic conditions, coarse constraints guide token generation during planning, while fine-grained constraints are enforced during control through diffusion-based optimization. This design prevents kinematic details from disrupting semantic token planning. On HumanML3D, our method significantly improves controllability and fidelity over MaskControl while using only one-sixth of the tokens, reducing trajectory error from 0.72 cm to 0.08 cm and FID from 0.083 to 0.029. Unlike prior methods that degrade under stronger kinematic constraints, ours improves fidelity, reducing FID from 0.033 to 0.014.

Related papers

• ScalingAttention: Discovering Intrinsic Sparse Attention Topology for Video Diffusion Transformers [7.879917783645986]
  Diffusion Transformers (DiTs) have revolutionized high-fidelity video generation, their reliance on 3D full attention creates a quadratic computational bottleneck.<n>Existing sparse methods face a dilemma: dynamic pruning suffers from prohibitive runtime overhead and memory fragmentation, while statics fail to capture fine-grained dependencies.<n>In this work, we propose ScalingAttention, a training-free framework grounded in a key inductive bias.<n> Experiments on Wan2.1 show up to 1.90X end-to-end speedup with superior fidelity, establishing a new frontier over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-06-22T08:32:07Z)
• Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control [51.92884966472683]
  MSCoT is a multi-scale, coarse-to-fine model for test-time human motion synthesis and control.<n> MSCoT discretizes motion into a multi-scale hierarchical representation and predicts the entire token sequence at each temporal scale.
  arXiv  Detail & Related papers  (2026-05-14T15:09:49Z)
• Causal Autoregressive Diffusion Language Model [70.7353007255797]
  CARD reformulates the diffusion process within a strictly causal attention mask, enabling dense, per-token supervision in a single forward pass.<n>Our results demonstrate that CARD achieves ARM-level data efficiency while unlocking the latency benefits of parallel generation.
  arXiv  Detail & Related papers  (2026-01-29T17:38:29Z)
• Repulsor: Accelerating Generative Modeling with a Contrastive Memory Bank [65.00301565190824]
  mname is a plug-and-play training framework that requires no external encoders.<n>mname achieves a state-of-the-art FID of textbf2.40 within 400k steps, significantly outperforming comparable methods.
  arXiv  Detail & Related papers  (2025-12-09T14:39:26Z)
• $\mathcal{E}_0$: Enhancing Generalization and Fine-Grained Control in VLA Models via Continuized Discrete Diffusion [65.77755100137728]
  We introduce E0, a continuized discrete diffusion framework that formulates action generation as iterative denoising over quantized action tokens.<n>E0 achieves state-of-the-art performance across 14 diverse environments, outperforming strong baselines by 10.7% on average.
  arXiv  Detail & Related papers  (2025-11-26T16:14:20Z)
• From Tokens to Nodes: Semantic-Guided Motion Control for Dynamic 3D Gaussian Splatting [26.57713792657793]
  We propose a motion-adaptive framework that aligns control density with motion complexity.<n>We show significant improvements in reconstruction quality and efficiency over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-03T05:33:58Z)
• Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling [87.34677262370924]
  Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token.<n>This creates an 'information void' where semantic information that could be inferred from unmasked tokens is lost between denoising steps.<n>We introduce Continuously Augmented Discrete Diffusion, a framework that augments the discrete state space with a paired diffusion in a continuous latent space.
  arXiv  Detail & Related papers  (2025-10-01T18:00:56Z)
• DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding [29.643549839940025]
  We introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding.<n>Our core idea is to frame token decoding as a conditional generation task.<n>DisCoRD achieves state-of-the-art performance, with FID of 0.032 on HumanML3D and 0.169 on KIT-ML.
  arXiv  Detail & Related papers  (2024-11-29T07:54:56Z)
• MaskControl: Spatio-Temporal Control for Masked Motion Synthesis [46.20712809159041]
  We propose MaskControl, the first approach to introduce controllability to the generative masked motion model.<n>First, textitLogits Regularizer implicitly perturbs logits at training time to align the distribution of motion tokens with the controlled joint positions.<n>Second, textitLogit optimization explicitly reshapes the token distribution that forces the generated motion to accurately align with the controlled joint positions.
  arXiv  Detail & Related papers  (2024-10-14T17:50:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.