CoMoGen: COntrollable MOtion Dynamics and Interactions with Mask-Guided Video GENeration
CoMoGen is a controllable video generation framework that generates realistic interactive dynamics from a single binary mask sequence conditioned on an input image. It introduces a lightweight MaskAdapter to encode mask sequences into latent residual signals injected into a Multi Modal Diffusion Transformer (MMDiT) via a cosine-weighted schedule. By identifying 'Motion Layers' in the attention space of MMDiT and fine-tuning only those layers with Low-Rank Adaptation (LoRA), CoMoGen reduces computational cost without architectural changes. Experiments show state-of-the-art performance in motion fidelity and perceptual realism.
Article intelligence
Key points
- CoMoGen enables controllable video generation from a binary mask sequence and an input image.
- Introduces MaskAdapter and Motion Layers for efficient motion injection.
- Uses LoRA to selectively fine-tune Motion Layers without modifying MMDiT architecture.
- Outperforms prior methods in motion fidelity and interaction plausibility.
Why it matters
This matters because coMoGen enables controllable video generation from a binary mask sequence and an input image.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.22996] CoMoGen: COntrollable MOtion Dynamics and Interactions with Mask-Guided Video GENeration
[Submitted on 21 May 2026]
Title:CoMoGen: COntrollable MOtion Dynamics and Interactions with Mask-Guided Video GENeration
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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. Project page: this http URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.22996 [cs.CV]
(or arXiv:2605.22996v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.22996
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Adil Meric [view email] [v1] Thu, 21 May 2026 19:51:20 UTC (3,911 KB)
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