CoMo: Controllable Motion Generation through Language Guided Pose Code Editing

Core Concepts

CoMo introduces a Controllable Motion generation model that accurately generates and edits motions by leveraging large language models, achieving competitive performance in motion generation and surpassing previous work in motion editing abilities.

Abstract

The content introduces CoMo, a model for controllable motion generation through language-guided pose code editing. It addresses the limitations of existing approaches by allowing fine-grained control over the generation process. CoMo decomposes motions into pose codes, enabling accurate motion editing based on textual inputs. The model consists of three main components: Motion Encoder-Decoder, Motion Generator, and Motion Editor. CoMo achieves competitive performance in text-driven motion generation compared to state-of-the-art models and excels in human studies for motion editing abilities. Introduction Text-to-motion models lack fine-grained controllability. CoMo introduces a Controllable Motion generation model. Challenges in human motion synthesis due to diverse behaviors. Methodology CoMo decomposes motions into discrete pose codes. Components include Motion Encoder-Decoder, Generator, and Editor. Utilizes large language models for precise motion editing. Experiments & Results Competitive performance on HumanML3D and KIT datasets. Human evaluation shows preference for CoMo in motion editing. Contributions include semantic motion representation and transformer-based model.

Stats

Experiments demonstrate that CoMo achieves competitive performance in text-driven motion generation compared to state-of-the-art models while substantially surpassing previous work in human studies for motion editing abilities.

Quotes

"CoMo allows for intuitive, language-controlled adjustments to the motion sequences." "Experiments demonstrate that CoMo achieves competitive performance in motion generation."

Key Insights Distilled From

CoMo

by Yiming Huang... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.13900.pdf

Deeper Inquiries

How can the incorporation of physical priors enhance zero-shot motion editing capabilities?

Incorporating physical priors in zero-shot motion editing can improve the realism and feasibility of edited motions. By integrating knowledge about physical constraints, such as joint limits, muscle dynamics, and gravity effects, into the editing process, the system can ensure that the generated motions are physically plausible. This helps in maintaining consistency and coherence in the edited sequences by preventing unrealistic or unnatural movements. Additionally, leveraging physical priors allows for more accurate adjustments to pose codes based on editing instructions, leading to smoother transitions between poses and ensuring that the final edited motions align with human biomechanics.

What are the implications of expanding keywords and pose codes to include global descriptors?

Expanding keywords and pose codes to encompass global descriptors introduces a broader range of information that can be utilized for text-driven motion generation and editing. Global descriptors provide context about overall characteristics of a motion sequence, such as speed, style, trajectory patterns, or emotional expressions. By incorporating these global descriptors into keyword generation and pose code representation, CoMo can capture higher-level attributes that influence the entire motion sequence rather than focusing solely on local kinematic details. This expansion enables more comprehensive control over generated motions by considering not only individual body part movements but also overarching features that define the essence of a particular action.

How does CoMo balance complexity with reconstruction quality when varying codebook sizes?

CoMo balances complexity with reconstruction quality by carefully selecting an optimal codebook size that captures sufficient detail while avoiding unnecessary computational burden. When varying codebook sizes during ablation studies, CoMo evaluates how different numbers of pose codes impact motion reconstruction performance. A larger codebook size increases granularity in capturing specific nuances of body part movements but may lead to increased computational overhead during training and inference due to higher-dimensional representations. On the other hand, a smaller codebook size simplifies model architecture but risks losing fine-grained details essential for accurate reconstruction. By striking a balance between complexity (determined by codebook size) and reconstruction quality (measured through fidelity metrics), CoMo identifies an ideal setting where it achieves both high-quality motion synthesis/editing results without compromising efficiency or interpretability.