# COMO: Compositional Motion Customization for Text-to-Video Models

[Anonymous]

#### Input Videos

Single-Motion (burpee)
Single-Motion (dance)
Multi-Motion (Compose burpee)
Multi-Motion (Compose dance)
Multi-Motion (Compose burpee and dance)
Compose

[Paper] [Code]

TL;DR- We introduces CoMo, a novel framework for compositional motion customization in text-to-video generation, enabling the synthesis of multiple, distinct motions within a single video.

# Abstract

While recent text-to-video models excel at generating diverse scenes, they struggle with precise motion control, particularly for complex, multi-subject motions. Although methods for single-motion customization have been developed to address this gap, they fail in compositional scenarios due to two primary challenges: motion-appearance entanglement and ineffective multi-motion blending . This paper introduces CoMo, a novel framework for compositional motion customization in text-to-video generation, enabling the synthesis of multiple, distinct motions within a single video. CoMo addresses these issues through a two-phase approach. First, in the single-motion learning phase, a static-dynamic decoupled tuning paradigm disentangles motion from appearance to learn a motion-specific module. Second, in the multi-motion composition phase, a plug-and-play divide-and-merge strategy composes these learned motions without additional training by spatially isolating their influence during the denoising process. To facilitate research in this new domain, we also introduce a new benchmark and a novel evaluation metric designed to assess multi-motion fidelity and blending. Extensive experiments demonstrate that CoMo achieves state-of-the-art performance, significantly advancing the capabilities of controllable video generation.

# An overview of our proposed CoMo framework

Our method consists of two phase:
Decoupled single-motion learning – we first train a static LoRA module on random frame to learn the appearance of the reference video. Then, we freeze the static LoRA and train a dynamic LoRA module on the complete video to exclusively capture its motion patterns. focusing solely on learning appearance.
plug-and-play multi-motion composition – we introduce a divide-and-merge strategy for compositional motion generation, while all the weights are frozen.

# Merge process

The final velocity prediction is generated by merging regional velocities with a Gaussian Smooth Transition for local blending, followed by a Global Blending step to ensure overall consistency.

# Single Motion Customization Results

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*Tap the navigation dots above or swipe left/right on your mobile device to browse the videos.

#### Reference Video (dance)

monkey dance
alien dance
bear dance
lion dance
robot dance
monkey dance
bear dance
polar bear dance

# Compositional Motion Customization Results

#### Reference Videos (burpee, dance, exercise)

Compose burpee ,dance and exercise
Compose burpee and exercise
Compose burpee ,dance and exercise
Compose burpee and dance
Compose dance and exercise
Compose burpee
Compose dance
Compose exercise

# Experiments

#### single motion customizatio Comparison

#### Compositional motion customization Comparison

#### Ablation

# More results

#### Reference Videos

dance
Compose dance
handstand
frog
monkey
susqirrel
handstand
Compose handstand
run
Compose run
Compose run
Circling counterclockwise
Circling counterclockwise
Run
Run
Riding
Riding

Our project page is borrowed from Dynamic Concepts. Thanks for their great work!

## BibTeX Citation

        @misc{,
          title={},
          author={},
          year={},
          eprint={},
          archivePrefix={},
          primaryClass={}
      }