CoMoGen: COntrollable MOtion Dynamics and Interactions With Mask-Guided Video GENeration

MCML Authors

Mert Kiray

→ Group Benjamin Busam
 Photogrammetry and Remote Sensing

Benjamin Busam

Prof. Dr.

Core PI

Photogrammetry and Remote Sensing

Abstract

We present CoMoGen, a controllable video generation framework that generates realistic interactive dynamics from a single binary mask sequence conditioned on an input image. CoMoGen introduces a lightweight MaskAdapter that encodes binary mask sequences into a latent residual signal, injected into the Multi Modal Diffusion Transformer (MMDiT) model through a cosine-weighted schedule. Unlike the hierarchical coarse-to-fine design of UNet architectures, MMDiT operates as a sequence of uniform transformer blocks, making it difficult to identify which layers are responsible for the motion generation. Therefore, we propose a novel way to determine 'Motion Layers' operating in the attention space of MMDiT. We fine-tune the model by using Low-Rank Adaptation (LoRA) to the Motion Layers, without requiring any architecture change in the MMDiT. This selective adaptation enables our method to focus on motion-critical components, yielding reduced computational cost. Despite its simplicity, CoMoGen enables precise subject motion and plausible interactions with surrounding humans, objects, and scenes. Comprehensive experiments on different datasets show that CoMoGen consistently outperforms prior controllable video generation methods and achieves state-of-the-art performance in motion fidelity and perceptual realism.

misc MFK+26a

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Preprint

May. 2026

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Authors

A. Meric • L. G. Foo • M. Kiray • B. Busam • R. Dabral • C. Theobalt

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Links

arXiv URL

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Research Area

 B1 | Computer Vision