👋 Hi, I’m Ruihao Xia, a Ph.D. candidate (expected 2026). I’m seeking internship and full-time opportunities in AIGC, 3D vision, and multimodal intelligence. More about me and my CV: https://xiarho.github.io/ — feel free to reach out if my background aligns with your team!

In this paper, we introduce 3DEditVerse, the largest paired 3D editing benchmark, and propose 3DEditFormer, a mask-free transformer enabling precise, consistent, and scalable 3D edits.

🌞 3DEditVerse

Our 3DEditVerse, the largest paired 3D editing benchmark to date, comprising 116,309 high-quality training pairs and 1,500 curated test pairs.

✨ 3DEditFormer

Our 3DEditFormer, a 3D-structure-preserving conditional transformer, enabling precise and consistent edits without requiring auxiliary 3D masks.

1. Our environment setup follows the official TRELLIS project.
   Please refer to their installation instructions for dependency versions and CUDA/PyTorch configurations.

2. Install the blender: Download from https://download.blender.org/release/Blender4.4/blender-4.4.3-linux-x64.tar.xz and extract it.

1. Download our 3DEditVerse dataset: 3DEditVerse. About 227 GB (636,569 files).

2. Extract the *.tar files in the 3DEditVerse folder.

tar -xf alpaca.tar / mixamo.tar / test_data.tar

• For flux_edit.part.tar.* files, you should concatenate them into a single file before extracting.

cat flux_edit.part.tar.* > flux_edit.tar

3. The data folder structure should look like this:

path_to_3DEditVerse/3DEditVerse
├── alpaca
│   ├── 1
│   ├── 2
│   ├── ...
├── flux_edit
│   ├── 3D CG rendering_4
│   ├── 3D CG rendering_5
│   ├── ...
├── mixamo
│   ├── latents
│   ├── renders_cond
│   ├── ss_latents
├── test_data
│   ├── alpaca
│   ├── alpaca_render
│   ├── flux_edit
│   ├── flux_edit_render
│   ├── mixamo
│   ├── mixamo_render
├── alpaca_confidence.json
├── flux_edit_confidence.json
├── dataset_info.json
├── test_data_info.json
├── edit_prompts.json

1. Download the trained model of 3DEditFormer and put them in the ./work_dirs/Editing_Training folder. Then, you can inference on the testing data in 3DEditVerse:

CUDA_VISIBLE_DEVICES=0 python eval_3d_editing.py --cuda_idx 0 --world_size 1 --rank 0 --dataset_root_dir /path_to_3DEditVerse/3DEditVerse --blender_path /path_to_blender/blender-4.4.3-linux-x64/blender --ss_latents_load_id img_to_voxel --latents_load_id voxel_to_texture --save_name 3DEditFormer --output_mesh --output_video --print_time

• In the above command, replace /path_to_3DEditVerse/3DEditVerse with the path to your 3DEditVerse dataset and /path_to_blender/blender-4.4.3-linux-x64/blender with the path to your blender. CUDA_VISIBLE_DEVICES=0 means the GPU index for model inference, --cuda_idx 0 means the GPU index for image rendering with blender.
• You can change the --world_size and --rank to inference the model on multiple GPUs, i.e., run the command with the same --world_size 4 and different --rank 0/1/2/3 on 4 GPUs.

2. Calculate the 2D metrics based on the rendered images (rendered from predicted 3D meshes):

CUDA_VISIBLE_DEVICES=0 python calculate_metric_2d.py --eval_results_dir ./work_dirs/eval_results/3DEditFormer --dataset_root_dir /path_to_3DEditVerse/3DEditVerse

• The metrics will be saved in ./work_dirs/eval_results/3DEditFormer/eval_metric.json.

3. Calculate the 3D metrics based on the predicted 3D meshes:

CUDA_VISIBLE_DEVICES=0 python calculate_metric_3d.py --eval_results_dir ./work_dirs/eval_results/3DEditFormer --dataset_root_dir /path_to_3DEditVerse/3DEditVerse

• The metrics will be saved in ./work_dirs/eval_results/3DEditFormer/eval_metric.json.

1. The first stage: generation of coarse voxelized shapes

CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=12349 train_torchrun.py --config configs/editing/ss_flow_img_dit_L_16l8_fp16.json --data_dir /path_to_3DEditVerse/3DEditVerse --output_dir ./work_dirs/Editing_Training/img_to_voxel_01 --random_cond_gt --train_only_editing_weights --lr 0.0001 --max_steps 40000 --batch_size_per_gpu 4 --random_ori_edit 0.15 --simple_edit_data_if_filtered

2. The second stage: generation of fine-grained texture

CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=12349 train_torchrun.py --config configs/editing/slat_flow_img_dit_L_64l8p2_fp16.json --data_dir /path_to_3DEditVerse/3DEditVerse --output_dir ./work_dirs/Editing_Training/voxel_to_texture_01 --random_cond_gt --train_only_editing_weights --lr 0.0001 --max_steps 40000 --batch_size_per_gpu 4

• Interactive 3D editing demo.
• Visualize the 3DEditVerse dataset.

Thanks TRELLIS, VoxHammer for their public code and released models.

If you find this project useful, please consider citing:

@article{3DEditFormer,
  title={Towards Scalable and Consistent 3D Editing},
  author={Xia, Ruihao and Tang, Yang and Zhou, Pan},
  journal={arXiv:2510.02994},
  year={2025}
}