You can deploy this model and start earning money today!

lanikoworld/
3DreamBooth-CustomBench

Name: 3DreamBooth-CustomBench
Availability: InStock

video-generation

subject-driven-generation

lora

dreambooth

2603.18524

tencent/HunyuanVideo-1.5

adapter

apache-2.0

Views

No views yet

3DreamBooth-CustomBench Checkpoints

Pretrained joint (3DreamBooth + 3Dapter) LoRA checkpoints for all 30 subjects in 3D-CustomBench, released alongside 3DreamBooth so you can run subject-driven video generation without training from scratch.

Each subject was trained for 400 steps with train_joint.py. Folder names match the official 3D-CustomBench subject ids exactly.

text

subjects.json                                           # trigger phrase + metadata for all 30 subjects
<subject_id>/
├── subject_adapter/pytorch_lora_weights.safetensors     # 3DreamBooth identity LoRA
├── tdapter/pytorch_lora_weights.safetensors             # subject-specific 3Dapter LoRA
└── training_config.json                                 # training args for this subject

Trigger phrases

Every subject uses the rare identifier rhs followed by a class word that differs per subject — rhs bear, rhs mug, rhs bust, and so on. This phrase is what carries the learned identity, so it must appear verbatim in your inference prompt and be marked as the LoRA span. Using the wrong class word (e.g. rhs plushie for a checkpoint trained on rhs bear) will not activate the subject identity.

subjects.json is the machine-readable source for this:

python

import json
from huggingface_hub import hf_hub_download

meta = json.load(open(hf_hub_download(
    "lanikoworld/3DreamBooth-CustomBench", "subjects.json")))
spans = {s["subject_id"]: s["lora_span"] for s in meta["subjects"]}
print(spans["graduation_bear"])   # "rhs bear"

Each entry carries subject_id, lora_span, identifier, class_word, the exact train_prompt used, and the subject's benchmark_prompt from 3D-CustomBench.

Subjects and prompt spans

Subject id	LoRA span	Training prompt
`bear_keychain`	`rhs plushie`	`A video of a [rhs plushie].`
`black_gold_mug`	`rhs mug`	`A video of a [rhs mug].`
`black_handbag`	`rhs handbag`	`A video of a [rhs handbag].`
`blue_label_pill_bottle`	`rhs bottle`	`A video of a [rhs bottle].`
`blue_pig_mug`	`rhs mug`	`A video of a [rhs mug].`
`cat_figurine`	`rhs figurine`	`A video of a [rhs figurine].`
`ceramic_bust`	`rhs bust`	`A video of a [rhs bust].`
`covered_motorcycle`	`rhs motorbike`	`A video of a [rhs motorbike].`
`deer_flowerpot`	`rhs pot`	`A video of a [rhs pot].`
`drawstring_pants`	`rhs sweatpants`	`A video of a [rhs sweatpants].`
`floral_mug`	`rhs mug`	`A video of a [rhs mug].`
`gaming_headset`	`rhs headset`	`A video of a [rhs headset].`
`graduation_bear`	`rhs bear`	`A video of a [rhs bear].`
`hand_cream`	`rhs tube`	`A video of a [rhs tube].`
`headband_bust`	`rhs bust`	`A video of a [rhs bust].`
`lavender_pitcher`	`rhs pitcher`	`A video of a [rhs pitcher].`
`lotion_bottle`	`rhs bottle`	`A video of a [rhs bottle].`
`milk_carton`	`rhs carton`	`A video of a [rhs carton].`
`moose_plush`	`rhs plush`	`A video of a [rhs plush].`
`multifunction_printer`	`rhs printer`	`A video of a [rhs printer].`
`office_chair`	`rhs chair`	`A video of a [rhs chair].`
`pink_plush`	`rhs plush`	`A video of a [rhs plush].`
`pink_rubber_duck`	`rhs duck`	`A video of a [rhs duck].`
`rattan_light_bulb`	`rhs bulb`	`A video of a [rhs bulb].`
`small_pill_bottle`	`rhs bottle`	`A video of a [rhs bottle].`
`textured_rock`	`rhs rock`	`A video of a [rhs rock].`
`toy_toilet`	`rhs toilet`	`A video of a [rhs toilet].`
`wafer_bag`	`rhs bag`	`A video of a [rhs bag].`
`white_light_bulb`	`rhs bulb`	`A video of a [rhs bulb].`
`yogurt_drink`	`rhs bottle`	`A video of a [rhs bottle].`

subjects.json also records each subject's legacy_id — the internal name used during development — matching the legacy_id field in the dataset's manifest.json.

In every training_config.json, filesystem paths (pretrained_model_root, instance_data_root, reference_path, tdapter_path, output_dir) were rewritten from their original development locations to the public layout documented in the 3DreamBooth README. All hyperparameters and prompts are unmodified.

Usage

Download one subject:

bash

hf download lanikoworld/3DreamBooth-CustomBench \
  --local-dir ./checkpoints/custombench --include "graduation_bear/*"

Run inference with validate_joint.py:

bash

python validate_joint.py \
  --pretrained_model_root ./checkpoints/hunyuanvideo-1.5 \
  --pretrained_transformer_version 720p_t2v \
  --subject_adapter_path ./checkpoints/custombench/graduation_bear/subject_adapter/pytorch_lora_weights.safetensors \
  --tdapter_path ./checkpoints/custombench/graduation_bear/tdapter/pytorch_lora_weights.safetensors \
  --reference_path ./datasets/3d-custombench/subjects/graduation_bear/references \
  --prompt "A video of a rhs bear on a beach." \
  --text_lora_spans "rhs bear" \
  --video_length 81

See the main repo's Quick start section for the full setup (base model + 3Dapter checkpoint download, the config-driven scripts/run.py interface, and how the [rhs ...] bracket span format works).

License

Apache License 2.0, matching the main repository. Weights are derived from HunyuanVideo-1.5 fine-tuning and remain subject to the Tencent Hunyuan Community License.

Citation

bibtex

@misc{ko20263dreambooth,
  title         = {3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model},
  author        = {Hyun-kyu Ko and Jihyeon Park and Younghyun Kim and Dongheok Park and Eunbyung Park},
  year          = {2026},
  eprint        = {2603.18524},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2603.18524}
}

lanikoworld/3DreamBooth-CustomBench