Introduction
LAMP defines a motion domain-specific language (DSL), inspired by cinematography conventions. By harnessing program synthesis capabilities of LLMs, LAMP generates structured motion programs from natural language, which are deterministically mapped to 3D trajectories.
🎉 News
- Client inference is coming soon.
- Dec 7, 2025: Gradio demo is ready to use.
- Dec 7, 2025: We propose LAMP
⚙️ Installation
The codebase was tested with Python 3.11.13, CUDA version 12.8, and PyTorch >= 2.8.0
Setup for Model Inference
You can setup for LAMP model inference by running:
git clone https://github.com/mbkizil/LAMP.git && cd LAMP
pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128 # If PyTorch is not installed.
pip install -r requirements.txt
pip install wan@git+https://github.com/Wan-Video/Wan2.1
Download Models
Download the VACE and finetuned Qwen2.5-VL-7B-Instruct model weight using download.sh
chmod +x download.sh
./download.sh
🚀 Usage
In LAMP, users act as a director, providing natural language descriptions for both object and camera behaviors. The system translates these prompts into precise 3D Motion Programs and conditions the video generation process to produce cinematic shots.
Interactive Demo (Gradio)
To explore the full pipeline—from text-to-motion planning to final video synthesis—we provide an interactive Gradio interface. This single entry point handles the loading of the Motion Planner (Qwen2.5-VL) and the Video Generator (VACE) seamlessly.
python -m src.serve.app --model-path ./qwen_checkpoints/LAMP-Qwen-2.5-VL
This script will:
Load the LLM Motion Planner (Qwen2.5-based) into memory.
Initialize the embedded VACE pipeline for trajectory-conditioned generation.
Launch a local web server (default at http://127.0.0.1:8890).
💡Notes from VACE: (1) Please refer to vace/vace_wan_inference.py for the inference args. (2) For English language Wan2.1 users, you need prompt extension to unlock the full model performance. Please follow the instruction of Wan2.1 and set
--use_prompt_extendwhile running inference.
Acknowledgement
We are grateful for the following awesome projects that served as the foundation for LAMP, including VACE for the powerful all-in-one video generation backbone and Qwen for the robust language reasoning capabilities. We also extend our thanks to Qwen-VL-Series-Finetune, which provided an efficient framework for training our motion planner.
Additionally, we acknowledge the pioneering works in camera control and trajectory generation, specifically GenDoP and Exceptional Trajectories. Their contributions to motion datasets and evaluation methodologies have brought immense inspiration to this project and established essential baselines for the field of controllable video generation.
BibTeX
@misc{kizil2025lamplanguageassistedmotionplanning,
title={LAMP: Language-Assisted Motion Planning for Controllable Video Generation},
author={Muhammed Burak Kizil and Enes Sanli and Niloy J. Mitra and Erkut Erdem and Aykut Erdem and Duygu Ceylan},
year={2025},
eprint={2512.03619},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.03619},
}