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README

#

TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition

#####

Yongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang, Kui Jia

NeurIPS 2022 Spotlight

Paper | Project Page

News

:trophy: [Nov 11th 2022] TANGO was selected in NeurIPS spotlight papers.

Installation

Note: You can directly pull the image we uploaded to AliCloud

docker pull registry.cn-shenzhen.aliyuncs.com/baopin/t2m:1.7

System Requirements

  • Python >=3.7 and <=3.9
  • CUDA 11
  • Nvidia GPU with 12 GB ram at least
  • Open3d >=0.14.1
  • the package of clip (https://github.com/openai/CLIP)

Train

Call the below shell scripts to generate example styles.

# shoe made of gold
./demo/run_shoe_gold.sh
# vase made of wicker 
./demo/run_vase_wicker.sh
# car made of wood
./demo/run_car_wood_origin.sh
# ...

The outputs will be saved to results/demo

Validate

Call the below shell scripts to generate gif.

# shoe made of gold
./demo/test_shoe_gold.sh
# vase made of wicker 
./demo/test_vase_wicker.sh
# car made of wood
./demo/test_car_wood_origin.sh
# ...

Citation

@article{chen2022tango,
  title={Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition},
  author={Chen, Yongwei and Chen, Rui and Lei, Jiabao and Zhang, Yabin and Jia, Kui},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={30923--30936},
  year={2022}
}

Core symbols most depended-on inside this repo

save_gif
called by 6
eval.py
hemisphere_int
called by 4
sg_render.py
lambda_trick
called by 3
sg_render.py
compute_energy
called by 2
network.py
render_single_image
called by 2
neural_style_field.py
get_rbg_value
called by 2
neural_style_field.py
get_embedder
called by 2
embedder.py
fibonacci_sphere
called by 1
network.py

Shape

Function 19
Method 14
Class 5

Languages

Python100%

Modules by API surface

network.py11 symbols
embedder.py9 symbols
neural_style_field.py7 symbols
sg_render.py6 symbols
eval.py3 symbols
main.py2 symbols

For agents

$ claude mcp add tango \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact