We present a new part-based 3D generation framework FullPart that combines both implicit and explicit paradigms.

And we introduce the largest and most comprehensively annotated 3D part dataset PartVerse-XL, an upgraded version of PartVerse, from 12K/91K to 40K/320K objects/parts.

This repository is currently under development. Thank you for your patience.

1. Clone the repo:

   git clone https://github.com/hkdsc/fullpart.git
   cd src/submodule/TRELLIS

2. Follow the TRELLIS repo to install the basic dependencies:

   Before running the following command there are somethings to note:

   • By adding --new-env, a new conda environment named fullpart will be created. If you want to use an existing conda environment, please remove this flag.
   • By default the fullpart environment will use pytorch 2.4.0 with CUDA 11.8. If you want to use a different version of CUDA (e.g., if you have CUDA Toolkit 12.2 installed and do not want to install another 11.8 version for submodule compilation), you can remove the --new-env flag and manually install the required dependencies. Refer to PyTorch for the installation command.
   • If you have multiple CUDA Toolkit versions installed, PATH should be set to the correct version before running the command. For example, if you have CUDA Toolkit 11.8 and 12.2 installed, you should run export PATH=/usr/local/cuda-11.8/bin:$PATH before running the command.
   • By default, the code uses the flash-attn backend for attention. For GPUs do not support flash-attn (e.g., NVIDIA V100), you can remove the --flash-attn flag to install xformers only and set the ATTN_BACKEND environment variable to xformers before running the code. See the Minimal Example for more details.
   • The installation may take a while due to the large number of dependencies. Please be patient. If you encounter any issues, you can try to install the dependencies one by one, specifying one flag at a time.
   • If you encounter any issues during the installation, feel free to open an issue or contact us.

   Create a new conda environment named fullpart and install the dependencies:

   . ./setup.sh --new-env --basic --xformers --flash-attn --diffoctreerast --spconv --mipgaussian --kaolin --nvdiffrast

   The detailed usage of setup.sh can be found by running . ./setup.sh --help.

   Usage: setup.sh [OPTIONS]
   Options:
         -h, --help              Display this help message
         --new-env               Create a new conda environment
         --basic                 Install basic dependencies
         --train                 Install training dependencies
         --xformers              Install xformers
         --flash-attn            Install flash-attn
         --diffoctreerast        Install diffoctreerast
         --spconv                Install spconv
         --mipgaussian           Install mip-splatting
         --kaolin                Install kaolin
         --nvdiffrast            Install nvdiffrast
         --demo                  Install all dependencies for demo

3. Install other dependencies:

   cd ../../..
   pip install -r requirements.txt

The pretrained models will be uploaded here before Nov 26th.

1. Download the PartVerse/PartVerse-XL (12K/40K objects with 91K/320K parts) from HuggingFace. We have uploaded the initial dataset and are actively working on adding more. The data directory after decompressing the dataset should be as follows:

   dataset/
   ├── normalized_glbs/    
   ├── anno_infos/    
   ├── textured_part_glbs/ # Will be available before Nov 19th
   ├── text_captions.json  
   ├── train.csv
   ├── val.csv
   └── metadata.csv
   

   • normalized_glbs provides the complete, normalized 3D mesh of each object in GLB format. These are aligned with the part-level meshes and can be used for holistic shape analysis or comparison.

   • anno_infos provides part annotation information for each object.

     *_segmented.glb - full object without texture. 
     *_face2label.json - annotated part IDs for each face of the full objects. 
     *_info.json - auxiliary information for each part.
     

   • textured_part_glbs contains textured 3D meshes for each decomposed part of the objects. Each file is stored in the GLB format. (Will be available before Nov 19th.)

   • text_captions.json stores descriptive text captions for each part, automatically generated using a Vision-Language Model (VLM).

   • train.csv instance_id for train.

   • val.csv instance_id for eval.

   • metadata.csv metadata for Objaverse XL.

2. Preprocess

   Due to the large number of parts in some objects, we can discard some unimportant parts (such as a screw, etc.). We provide partverse/get_infos.py to process the data. By running it, you can obtain (1) some statistical information of the parts, (2) the priority of discarding them, (3) view of max overlap between full object and parts render.

   python partverse/get_infos.py --data_root ${DATA_PATH} --global_info_save_path ${SAVE_PATH} --max_visible_info_save_path ${SAVE_PATH}
   

3. Rendering

   We provide rendering script following TRELLIS. You can use partverse/render_parts.py to render textured_part_glbs (part objects) and partverse/render_dir.py to render normalized_glbs (whole objects), e.g.,

   # rendering the parts
   python partverse/render_parts.py --textured_part_glbs_root ${DATA_ROOT}/textured_part_glbs --out_dir ${DATA_ROOT}/textured_part_renders --num_views 150 --use_random_views --save_mesh
   # rendering the full objects
   python partverse/render_dir.py --input_dir ${DATA_ROOT}/normalized_glbs --output_dir ${DATA_ROOT}/textured_mesh_renders --num_views 150 --use_random_views --save_mesh
   # rendering the condition images
   python partverse/render_dir.py --input_dir ${DATA_ROOT}/normalized_glbs --output_dir ${DATA_ROOT}/renders_cond --num_views 24 --use_random_views

4. Extract latents

   We provide partverse/encode_latents_from_imgs.py to get slat latents following TRELLIS,

   # encoding the full object renders
   python partverse/encode_latents_from_imgs.py --data_root ${DATA_PATH} --mode full
   # encoding the part renders
   python partverse/encode_latents_from_imgs.py --data_root ${DATA_PATH} --mode part
   

5. Caption (optional)

   In addition, we also provide text caption code to facilitate users in customizing text prompts for their own models. For the VLM, we use Qwen2.5-VL-32B now. You can replace to any VLM.

   # rendering the reference images for captioning
   python partverse/render_parts.py --textured_part_glbs_root ${PART_GLB_PATH} --out_dir ${OUT_PATH} --num_views 8 --elevation 30
   # then generating text captions
   python partverse/get_text_caption.py --raw_img_root ${FULL_OBJECT_IMG_PATH} --part_img_root ${PART_IMG_PATH} --info_file ${MAX_VIS_INFO_PATH} --output_file ${OUT_PATH} --vlm_ckpt_dir ${VLM_HF_DOWN_PATH}
   

Edit the config file in src/configs/train_configs/personal_configs_part.py to set the required paths.

# personal_configs_part.py
// ...existing code...
train_dataset_csv_path = '{DATA_ROOT}/train.csv'
eval_dataset_csv_path = '{DATA_ROOT}/val.csv'
resume_ckpt_path = 'pretrained_model/{RESUME_CKPT_PATH}'
ss_flow_weights_dir = 'pretrained_models/trellis/ckpts'
part_dir='{DATA_ROOT}/anno_infos'
part_cond_dir='{DATA_ROOT}/renders_cond'
part_slat_dir='{DATA_ROOT}/textured_part_latents/dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16'
global_slat_dir='{DATA_ROOT}/textured_mesh_latents/dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16'
// ...existing code...

Then run the training script:

bash part_train.sh

Edit the config file in src/configs/train_configs/personal_configs_part_stage2.py to set the required paths.

# personal_configs_part_stage2.py
// ...existing code...
train_dataset_csv_path = '{DATA_ROOT}/train.csv'
eval_dataset_csv_path = '{DATA_ROOT}/val.csv'
resume_ckpt_path = 'pretrained_model/{RESUME_CKPT_PATH}'
ss_flow_weights_dir = 'pretrained_models/trellis/ckpts'
part_dir='{DATA_ROOT}/anno_infos'
part_cond_dir='{DATA_ROOT}/{RENDERS_COND}'
// ...existing code...

Then run the training script:

bash part_train_s2.sh

Additionally, we are currently working on integrating vecset diffusion into this repository, as it was originally trained on a different framework.

Run the following command:

python inference.py \
	--stage1.transformer-ckpt pretrained_models/{S1_CKPT_PATH} \
	--stage2.transformer-ckpt pretrained_models/{S2_CKPT_PATH} \
	--raw-image {MY_IMAGE_PATH} \
	--raw-box {MY_BOXES_PATH} \
	--raw-sample_id {MY_SAMPLE_ID} \
	--output-dir {OUT_PATH}

@misc{ding2025fullpart,
      title={FullPart: Generating each 3D Part at Full Resolution}, 
      author={Lihe Ding and Shaocong Dong and Yaokun Li and Chenjian Gao and Xiao Chen and Rui Han and Yihao Kuang and Hong Zhang and Bo Huang and Zhanpeng Huang and Zibin Wang and Dan Xu and Tianfan Xue},
      year={2025},
      eprint={2510.26140},
      archivePrefix={arXiv},
}