Upload README.md

README.md

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-license: cc
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+<p align="center">
+
+<h1 align="center">LAMP: Language-Assisted Motion Planning</h1>
+<p align="center">
+    <strong>M. Burak Kizil</strong>
+    ·
+    <strong>Enes Sanli</strong>
+    ·
+    <strong>Niloy J. Mitra</strong>
+    ·
+    <strong>Erkut Erdem</strong>
+    ·
+    <strong>Aykut Erdem</strong>
+    ·
+    <strong>Duygu Ceylan</strong>
+    <br>
+    <br>
+        <a href="https://arxiv.org/abs/2512.03619">arXiv</a>&nbsp;&nbsp;&nbsp;
+        <a href="https://cyberiada.github.io/LAMP/">Webpage</a>&nbsp;&nbsp;&nbsp;
+        <a href="https://github.com/mbkizil/LAMP/">GitHub</a>
+    <br>
+</p>
+
+
+## Introduction
+<strong>LAMP</strong> 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.
+
+<img src='./assets/teaser.jpg'>
+
+
+## 🎉 News 
+- [ ] Client inference is coming soon.
+- [x] Dec 7, 2025: Gradio demo is ready to use. 
+- [x] Dec 7, 2025: We propose [LAMP](https://cyberiada.github.io/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:
+```bash
+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](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B) and finetuned [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) model weight using [download.sh](download.sh)
+
+
+```bash
+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.
+```bash
+
+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](./src/vace_lib/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](https://github.com/Wan-Video/Wan2.1?tab=readme-ov-file#2-using-prompt-extension) and set `--use_prompt_extend` while running inference.
+
+
+
+## Acknowledgement
+
+
+We are grateful for the following awesome projects that served as the foundation for LAMP, including [VACE](https://github.com/ali-vilab/VACE) for the powerful all-in-one video generation backbone and [Qwen](https://github.com/QwenLM/Qwen3-VL?ref=xxzz.info) for the robust language reasoning capabilities. We also extend our thanks to [Qwen-VL-Series-Finetune](https://github.com/2U1/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](https://github.com/3DTopia/GenDoP) and [Exceptional Trajectories](https://github.com/robincourant/DIRECTOR). 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
+
+```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}, 
+}