Overview
SeMoCo (Semantic-First Motion Codec) is introduced as a novel approach to discrete motion representation, designed to prioritize semantic understanding over general reconstruction accuracy in motion tokenization. This codec is specifically developed for application in language-conditioned motion generation, addressing a limitation in existing motion tokenizers that typically allocate capacity based on reconstruction efficiency without explicit consideration for semantic role. The framework integrates a dual-axis motion generator to process these semantic-first tokens effectively.
Research Context
The field of autoregressive text-to-motion generation has seen advancements through discrete motion representations. However, a noted characteristic of most prevalent motion tokenizers is their primary optimization for reconstruction. This design choice implies that both action-level meaning and fine-grained kinematic detail are encoded through a singular, reconstruction-driven hierarchy. The research identifies this as a point where capacity is not explicitly allocated according to the semantic role of motion, potentially limiting the effectiveness of motion tokens for downstream tasks requiring semantic understanding.
Approach
SeMoCo reconfigures the structure of motion tokens. Each token is designed to contain two distinct components: one semantic token and a subsequent residual sequence of kinematic tokens. This dual-component structure aims to explicitly separate semantic information from kinematic details within the discrete representation. Complementing this codec, a dual-axis motion generator is developed. This generator functions by modeling the semantic progression of motion across time, and then autoregressively refines the residual entries that capture fine-grained kinematic information. As part of this research, a large-scale, multi-source human-motion dataset, named $\Omega$-MotionVerse, was constructed. This dataset unifies diverse human motion data under the SOMA representation, providing a standardized base for the development and evaluation of motion language models.
Findings
- SeMoCo achieved the best reconstruction accuracy when compared against other codecs evaluated in the reported comparisons.
- The motion tokens generated by SeMoCo demonstrated effectiveness for downstream generation tasks, as indicated by strong text-to-motion results.
- The dual-axis generator effectively modeled semantic progression over time and refined residual kinematic entries.
- The $\Omega$-MotionVerse dataset unifies multi-source human motion data under the SOMA representation.
Why This Matters
The explicit allocation of capacity according to semantic role in motion tokenization, as implemented by SeMoCo, could enhance the fidelity and meaningfulness of generated motions from textual inputs. The reported superior reconstruction accuracy of SeMoCo suggests an improved foundational representation. Furthermore, the demonstrated effectiveness of its motion tokens for downstream generation, specifically text-to-motion, indicates a potential for more semantically coherent and detailed synthetic motion sequences, which could benefit applications relying on human motion simulation or animation.