Hydrilla/BlueFox3D-character-shape512

BlueFox3D character shape-512 (L4 finetune)

This is not a replacement for Hydrilla/BlueFox3D.
Default BlueFox3D inference still uses that stock repo.

This repo contains a shape-512 denoiser finetuned on a small character-like Objaverse-LVIS subset. All other cascade stages (sparse structure, shape-1024, texture-1024) are still loaded from stock Hydrilla/BlueFox3D.

What was trained

Item	Value
Stage	shape-512 only (ElasticSLatFlowModel, 30 blocks, 1.39B params)
Trainable	last 4 transformer blocks + out_layer (~183M / 1.39B)
Steps	1000
Batch	1 mesh / step
GPU	1× NVIDIA L4 24 GB
Optimizer	AdamW 8-bit, lr 1e-5, no EMA
Start weights	public Pixal3D / BlueFox3D shape-512

1536 inference still runs:

1. SS-64 — stock
2. Shape-512 — this file
3. Shape-1024 — stock
4. Texture-1024 — stock

Expect a small mid-stage geometry shift, not a new product model.

Dataset

Objaverse-LVIS only. We did not download the ~8.9 TB allenai/objaverse snapshot.

LVIS keys (no class named character): person, teddy bear, doll, figurine, statue, mannequin, puppet, action figure, toy soldier, snowman, scarecrow. Matching was a bit loose, so extra toy/bear IDs mixed in.

Split	Count	Trained on?
Train GLBs	150 downloaded, 149 encoded (1 mesh failed voxelize)	Yes
Hold-out GLBs	20	Never
Encoded	2 Blender views + dual-grid 512 + shape-512 view latents	Yes
PBR / SS latents	not built	No

One-image compare (honest)

Same photo assets/images/21_img.png (jester), seed 42, --preset baseline, --resolution 1536, --low_vram.

	Stock Pixal3D / BlueFox3D	This shape-512
Weights	Hydrilla/BlueFox3D	this repo for shape-512 only
Vertices	210,075	198,274
Faces	246,515	235,199

The two GLBs are not identical, but they look similar. This is not a quality-win claim. 21_img is a demo photo, not one of the 20 hold-out meshes.

Use

python inference.py --image assets/images/21_img.png --output ./ft.glb \
  --seed 42 --low_vram --resolution 1536 --preset baseline \
  --model_path Hydrilla/BlueFox3D-character-shape512

--low_vram is required on 24 GB GPUs.

Stock (unchanged):

python inference.py --image assets/images/21_img.png --output ./stock.glb \
  --seed 42 --low_vram --resolution 1536 --preset baseline \
  --model_path Hydrilla/BlueFox3D

Files

• pipeline.json — shape-512 from this repo; other stages from Hydrilla/BlueFox3D
• ckpts/slat_flow_img2shape_dit_1_3B_512_bf16.safetensors — converted denoiser_step0001000.pt
• ckpts/slat_flow_img2shape_dit_1_3B_512_bf16.json — same architecture as stock shape-512

Code

Training and convert scripts live in HydrillaAI/Bluefox3D.

License / citation

MIT, same as BlueFox3D. Upstream: Pixal3D / TRELLIS.2.

@article{li2026pixal3d,
    title={Pixal3D: Pixel-Aligned 3D Generation from Images},
    author={Li, Dong-Yang and Zhao, Wang and Chen, Yuxin and Hu, Wenbo and Guo, Meng-Hao and Zhang, Fang-Lue and Shan, Ying and Hu, Shi-Min},
    journal={arXiv preprint arXiv:2605.10922},
    year={2026}
}