Part-Level Motion Tokenization

• Part-Level Motion Tokenization is the process of converting continuous motion into discrete, semantically coherent tokens for easier interpretation and generation.
• It leverages techniques like vector quantization and specialized codebooks to achieve fine-grained tokenization of body parts, objects, or scene segments.
• This approach enables robust cross-modal translation, efficient video modeling, and improved generative synthesis for applications in animation and robotics.

Part-level motion tokenization refers to the process of discretizing continuous spatiotemporal motion—whether of the full human body, articulated object parts, or generic scene segments—into compact, informative, and semantically coherent token sequences. This operation, foundational to recent advances in cross-modal learning, generative modeling, and dynamic scene analysis, facilitates both computational tractability and improved semantic alignment in downstream tasks such as motion generation, captioning, video understanding, and editing. The technique underpins a diverse set of workflows including vector quantization models for 3D human motion (Guo et al., 2022), part-aware neural rendering for object segmentation and animation (Yang et al., 2023, Liu et al., 2023), and motion-guided token compressors for efficient video modeling (Zhang et al., 21 Mar 2025). In modern practice, part-level tokenization enables reciprocity between motion and language representations, robust category-agnostic object analysis, scalable transformer-based synthesis pipelines, and a range of applications from gaming to robotics.

1. Discretizing Motion with Vector Quantization

Part-level motion tokenization commonly leverages VQ-VAE (Vector Quantized Variational Autoencoder) frameworks or similar quantization schemes for transforming high-dimensional continuous motion signals into discrete token sequences. For human motion, a canonical instance is TM2T (Guo et al., 2022), where full-body pose trajectories $m \in \mathbb{R}^{T \times D_p}$ are first mapped to lower-dimensional feature streams via a 1D convolutional encoder $E(m)$, and then quantized:

$$b_{q,i} = \arg \min_{b_k \in \mathcal{S}} \| \hat{h}_i - b_k \|$$

where $\mathcal{S}$ denotes the learnable codebook of prototypical motion vectors ("tokens"). The motion token sequence $s \in \{1,2,\ldots,|\mathcal{S}|\}^t$ abstracts local spatiotemporal patterns, enforcing compactness and semantic interpretability.

In part-based VQ-VAEs, this process is generalized: different body parts or object parts (such as right/left limbs, torso, or even facial regions) are encoded and discretized with specialized, possibly separate codebooks (Zhou et al., 2023, Zou et al., 27 Mar 2024, Ling et al., 26 Nov 2024, Liu et al., 13 Dec 2024). For instance, hand gestures and body postures can be tokenized independently, their tokens later fused or coordinated in a hierarchical manner (Zhou et al., 2023, Ling et al., 26 Nov 2024).

In scene or object-centric contexts, grid-based embeddings derived from multimodal sensor data (e.g., LiDAR + images) undergo clustering or quantization to yield scene part tokens, as evidenced in 3D part discovery (Yang et al., 2023, Mu et al., 30 Apr 2024).

2. Representation of Spatiotemporal Parts and Rigid Motions

Beyond abstracting motion, part-level tokenization structures these tokens to reflect rigid or deformable part movements, providing a basis for part manipulation, segmentation, or motion prediction. In MovingParts (Yang et al., 2023), dynamic 3D scenes are decomposed by analyzing the trajectories of scene points (Lagrangian view). Each “part” is a group of points sharing a consistent transformation over time, automatically factorized via soft or hard clustering of per-particle motion features. These groupings supply “motion tokens,” each now corresponding to an object part or articulated component, allowing explicit representation:

$x = R_L^{-1} \cdot (x_c + t_L)$

where $(R_L, t_L)$ encodes the rigid motion of a part. The framework’s cycle-consistency loss between Lagrangian and Eulerian representations ensures robustness and interpretability of the resulting part tokens.

In human and articulated object settings, explicit part tokenization (e.g., into arms, legs, root, etc.) enables fine-grained, interpretable modeling. ParCo (Zou et al., 27 Mar 2024) and similar architectures assign a transformer-based generator to each part, with part-specific VQ-VAE quantization, supporting coordinated yet independently controlled motion synthesis across the body.

3. Coordination, Reciprocation, and Cross-modal Translation

Tokenization creates a unified “language” for motion, facilitating both intra-modal (e.g., part-to-whole coordination) and inter-modal (motion-to-language, text-to-motion) tasks. In text-conditioned synthesis frameworks such as TM2T (Guo et al., 2022) and ParCo (Zou et al., 27 Mar 2024), both motion and language are tokenized and mapped bidirectionally through sequence-to-sequence models, much as in neural machine translation. The autoregressive modeling allows for stochastic, variable-length motion generation tied closely to text semantics.

ParCo further introduces a part-coordination mechanism—each part token predictor is synchronously conditioned on the token predictions of other parts to ensure temporal and anatomical coherence, implemented as:

$x^i_{coord} = \text{LN}(x^i + \text{MLP}^i(y))$

with $y$ pooling representations from all other part branches, ensuring that the motions of arms, legs, and torso are both semantically valid and physically synchronized.

Reciprocal frameworks are also used in inverse-alignment loss settings (Guo et al., 2022), enforcing mutual consistency between synthesized text and re-synthesized motion from the generated text, resulting in robust and semantically-anchored token spaces.

4. Compression, Efficiency, and Representation Scalability

Tokenization provides not only semantic and generative benefits but also substantial computational efficiency and scalability, critical for large-scale video or multi-agent processing (Zhang et al., 21 Mar 2025). Token Dynamics (Zhang et al., 21 Mar 2025) introduces dynamic clustering of spatial–temporal tokens, disentangling visual appearance (aggregated into a token “hash table”) from grid-level motion (tracked in a “dynamics map”). The cross-dynamics attention module then fuses motion cues into the base tokens, achieving extreme token reduction (down to 0.07% of originals) with only minor performance degradation—a key advance for efficient transformer-based video LLMs.

Compression approaches such as MGTC (Feng et al., 10 Jan 2024) prune tokens with low inter-frame motion variance, minimizing redundancy and focusing modeling capacity on dynamic regions. Such strategies support higher FPS rates and improved performance on action recognition with substantial savings in computational cost.

5. Applications: Generation, Editing, Prediction, and Understanding

Part-level motion tokenization underlies cutting-edge applications:

• Motion/Text Generation and Captioning: By forming a “motion vocabulary,” models like TM2T (Guo et al., 2022), MotionGPT (Jiang et al., 2023), and MotionGPT-2 (Wang et al., 29 Oct 2024) achieve reciprocal conversion tasks, supporting diverse, text-conditioned motion synthesis as well as accurate motion description from pose data.
• Object and Scene Discovery: Motion-guided tokenization with attention and vector quantization fosters unsupervised part discovery, yielding interpretable and memory-efficient representations for clustering, segmentation, and tracking (Bao et al., 2023, Yang et al., 2023).
• Animation and Interactive Editing: Part-aware tokenization with fine-grained control over articulated parts enables applications such as DragAPart (Li et al., 22 Mar 2024) and Puppet-Master (Li et al., 8 Aug 2024) for interactive image/video editing in response to user-specified "drags" or part trajectories, generalizing well across object categories via domain-randomized training.
• Efficient Robotic Manipulation and Affordance Analysis: By encoding part-level motion priors, frameworks such as PARIS (Liu et al., 2023) support scene editing, 3D object manipulation, and function understanding, as required in robotics.

6. Limitations, Challenges, and Future Directions

Despite demonstrated advances, several challenges remain. The granularity of tokenization presents a fundamental trade-off: finer tokens preserve detail but risk local dependency issues (e.g., short-term over-coherence), while coarser tokens dilute motion nuance (Jin et al., 22 Jun 2025). PlanMoGPT addresses this via progressive planning—hierarchically generating motion from global “plan” tokens, then refining to full sequences, with a flow-enhanced decoding stage to recover detail.

Generalization to diverse domains—such as multilingual talking heads (Liu et al., 13 Dec 2024), non-human agents, or complex open-world scenes (Ding et al., 15 May 2025, Mu et al., 30 Apr 2024)—requires both large, high-quality datasets and robust quantization schemes. Methods that disentangle visual from motion information and leverage part-aware hierarchies appear promising here.

Scalability to long sequences or extreme compression rates is an active area, with dynamic clustering and cross-attention approaches showing considerable promise (Zhang et al., 21 Mar 2025).

Future research is expected to focus on:

• Hierarchical and progressive planning across multi-part tokens.
• Adaptive and dynamic tokenization informed by downstream task requirements.
• Further unification of motion tokens with other modalities (music, speech, semantics) in general-purpose LLMs (Ling et al., 26 Nov 2024, Wang et al., 29 Oct 2024).
• Integration with physics-based and causal priors for grounded, physically plausible synthesis and analysis.

7. Summary Table: Key Elements of Part-Level Motion Tokenization

Approach/Paper	Tokenization Strategy	Core Application/Strength
TM2T (Guo et al., 2022)	VQ-VAE, compact motion tokens	Stochastic reciprocal text-motion gen
MovingParts (Yang et al., 2023)	Motion-based, part grouping/clustering	3D part discovery, tracking, animation
ParCo (Zou et al., 27 Mar 2024)	Part-wise VQ-VAE + coordination	Synchronized multi-part synthesis
Token Dynamics (Zhang et al., 21 Mar 2025)	K-Means, cross-dynamics attention	Extreme video token compression
PARIS (Liu et al., 2023)	Decoupled implicit radiance fields	Part motion param. est., re-rendering
Puppet-Master (Li et al., 8 Aug 2024)	Video diffusion, drag-conditioned	Interactive part-level video gen
MotionGPT(-2) (Jiang et al., 2023, Wang et al., 29 Oct 2024)	Tokenization + LLM unification	Multi-modal motion-language tasks

Part-level motion tokenization thus constitutes a foundational paradigm, with discrete, semantically structured tokens serving as a universal currency for understanding, generating, and controlling motion across diverse domains and modalities.