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IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination

Project Page | Paper | Supplementary | Hugging Face

IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination <br> Xi Chen, Sida Peng, Dongchen Yang, Yuan Liu, Bowen Pan, Chengfei Lv, Xiaowei Zhou<br>

News

• 2024-5-22: 🤗 Live demo released at https://huggingface.co/spaces/LittleFrog/IntrinsicAnything.
• 2024-5-31: Code release for single-view inference.

Results

Intrinsic Decomposition

Single view Relighting

https://github.com/zju3dv/IntrinsicAnything/assets/62891073/2e95855c-6d72-4bcb-8c79-577e55e6c926

Single view inference

1. Installation

conda create -n anyintrinsic python=3.10
conda activate anyintrinsic
pip install -r requirements.txt

2. Download the pre-trained diffusion models from hugghing face as follow:

# albedo checkpoint
huggingface-cli download --repo-type space --cache-dir  weights/albedo/checkpoints/ LittleFrog/IntrinsicAnything weights/albedo/checkpoints/last.ckpt 

# specular shaing checkpoint
huggingface-cli download --repo-type space --cache-dir  weights/specular/checkpoints/ LittleFrog/IntrinsicAnything weights/specular/checkpoints/last.ckpt 

3. Run inference to get intrinsic images:

python inference.py \
--input_dir  examples  \
--model_dir  weights/albedo \
--output_dir out/albedo \
--ddim 100 \
--batch_size 4

Parameter explanation:

• --input_dir: Path to the folder containing test images. The foreground object mask can be supplied as either RGBA input or specify a mask directory through --mask_dir. The mask can be obtained interactively using tools like Segment-Anything. We also provide a script generate_masks.py to generate masks automatically based on Rembg. (Note: Scene-level images can be directly referenced without masks)

• --model_dir: Path to the pre-trained checkpoint. Set weights/albedo for the albedo generation and weights/specular for the specular shading generation.

• --output_dir: Output path for the generated samples.

• --batch_size: Inference batch size.

4. Optionally, you can generate high-resolution samples under the guidance of samples from step 3.

python inference.py \
--input_dir examples  \
--model_dir weights/albedo \
--output_dir out/albedo_high_res \
--ddim 200 \
--batch_size 4 \
--guidance_dir out/albedo \
--guidance 3  \
--splits_vertical 2 \
--splits_horizontal 2 \
--splits_overlap 1

Extra parameter explanation:

• --guidance_dir: Path to the low resolution output (step 3).
• --guidance: Guidance scale of low-resolution images. We impartially found a scale from 2 to 5 generally yields good results.
• --splits_vertical & --splits_horizontal: The number of splits in the vertical and horizontal direction.
• --splits_overlap: The number of overlaps evenly distributed between two adjacent patches. The final value of each pixel is the average of all overlapped patches to improve the patches' consistency.

Multiview Inverse Rendering

Comming Soon.

Citation

@article{chen2024intrinsicanything,
    title     = {IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination},
    author    = {Xi, Chen and Sida, Peng and Dongchen, Yang and Yuan, Liu and Bowen, Pan and Chengfei, Lv and Xiaowei, Zhou.},
    journal   = {arxiv: 2404.11593},
    year      = {2024},
    }