Video2LoRA: Unified Semantic-Controlled Video Generation via Per-Reference-Video LoRA （CVPR 2026 Findings）

Customizing a dedicated semantic LoRA for each reference video.

📄 arXiv | 🌐 Project Page (coming soon)

Official implementation of the paper "Video2LoRA: Unified Semantic-Controlled Video Generation via Per-Reference-Video LoRA".

Video2LoRA enables semantic video generation by dynamically predicting lightweight LoRA adapters from reference videos using a HyperNetwork, without requiring per-condition fine-tuning.

🔥 Highlights

Video2LoRA introduces a new paradigm for semantic-controlled video generation.

Instead of training separate models or LoRA adapters for each semantic condition (e.g., visual effects, camera motion, style), our framework predicts semantic-specific LoRA weights directly from a reference video.

Key features:

• 🎬 Reference-driven semantic video generation
• ⚡ Ultra-lightweight LoRA (<50 KB per semantic condition)
• 🧠 Transformer-based HyperNetwork for LoRA prediction
• 🌍 Strong zero-shot generalization
• 🧩 Unified framework across heterogeneous semantic controls

🧠 Method Overview

Video2LoRA consists of three key components:

1. LightLoRA Representation

We introduce LightLoRA, a compact LoRA formulation that decomposes the standard LoRA matrices:

$$ A = A_{\text{aux}} A_{\text{pred}}, \quad B = B_{\text{pred}} B_{\text{aux}} $$

Where:

• $A_{\text{aux}}, B_{\text{aux}}$: trainable auxiliary matrices
• $A_{\text{pred}}, B_{\text{pred}}$: predicted by the HyperNetwork

This design significantly reduces parameter size while preserving semantic adaptability.

Each semantic condition requires less than 50 KB parameters.

2. HyperNetwork for LoRA Prediction

A Transformer-based HyperNetwork predicts semantic-specific LoRA weights conditioned on a reference video.

Pipeline:

Reference Video
      ↓
3D VAE Encoder
      ↓
Spatio-temporal features
      ↓
Transformer Decoder
      ↓
Predicted LoRA weights

These predicted LoRA modules are injected into the frozen diffusion backbone.

3. End-to-End Diffusion Training

Unlike prior methods that require:

• pretrained semantic LoRA weights
• multi-stage training pipelines

Video2LoRA is trained end-to-end using only the standard diffusion objective.

🌍 Zero-Shot Semantic Generation

Video2LoRA generalizes well to unseen semantic conditions.

Even when encountering out-of-domain visual effects, the model can generate semantically aligned videos based on reference videos.

Example semantic controls include:

• visual effects (VFX)
• camera motion
• object stylization
• character transformations
• artistic styles

📂 Dataset

Video2LoRA follows the dataset format used in VideoX-Fun, which supports mixed image and video training with text descriptions.

Organize your dataset in the following structure:

project/
│
├── datasets/
│   ├── internal_datasets/
│   │
│   ├── train/
│   │    ├── 00000001.mp4
│   │    ├── 00000002.jpg
│   │    ├── 00000003.mp4
│   │    └── ...
│   │
│   └── json_of_internal_datasets.json

📝 JSON Annotation Format

[
  {
    "file_path": "train/00000001.mp4",
    "text": "A group of young men in suits and sunglasses walking down a city street.",
    "type": "video"
  },
  {
    "file_path": "train/00000002.jpg",
    "text": "A group of young men in suits and sunglasses walking down a city street.",
    "type": "image"
  }
]

⚙️ Installation

Clone repository

git clone https://github.com/BerserkerVV/Video2LoRA.git  
cd Video2LoRA

Create environment

conda create -n video2lora python=3.10
conda activate video2lora

Install dependencies

pip install -r requirements.txt

🚀 Training

Train Video2LoRA:

bash scripts/cogvideoxfun/train_lora.sh

Training setup:

🎥 Inference

Generate a video using a reference video:

bash examples/cogvideox_fun/run_predict_i2v.sh

📖 Citation

If you find our work useful, please cite:

@misc{wu2026video2loraunifiedsemanticcontrolledvideo,
      title={Video2LoRA: Unified Semantic-Controlled Video Generation via Per-Reference-Video LoRA}, 
      author={Zexi Wu and Qinghe Wang and Jing Dai and Baolu Li and Yiming Zhang and Yue Ma and Xu Jia and Hongming Xu},
      year={2026},
      eprint={2603.08210},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.08210}, 
}

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