# Bootstrap3d Eval

> Evaluates text-to-multi-view diffusion models on their ability to generate prompt-aligned, high-quality 4-view images and reconstruct consistent 3D objects. It measures image-text alignment and visual fidelity against a synthetic ground-truth distribution. Use when the user wants to benchmark on GPTeval3D, Synthetic GT Distribution, or asks about evaluating this task. Reports FID.

- Skill: `qhjqhj00/bootstrap3d-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/bootstrap3d-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/bootstrap3d-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/bootstrap3d-eval

---


# bootstrap3d-eval

> Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data — Zeyi Sun et al. (2024) (arXiv:2406.00093, 2024)

## What this evaluates

Evaluates text-to-multi-view diffusion models on their ability to generate prompt-aligned, high-quality 4-view images and reconstruct consistent 3D objects. It measures image-text alignment and visual fidelity against a synthetic ground-truth distribution.

## Datasets

- **GPTeval3D** — total 110; splits: test (110)
- **Synthetic GT Distribution** — total 30000; splits: gt (30000)

## Metrics

- `FID` **(primary)** — range: other (float)
  - Fréchet Inception Distance computes the Wasserstein-2 distance between two multivariate Gaussians fitted to feature distributions of generated and reference images. Lower is better.
- `CLIP Score` — range: other (float)
  - Mean cosine similarity between text prompt embeddings and generated image embeddings from a CLIP model. Higher is better.
- `CLIP-R Score` — range: other (float)
  - Mean cosine similarity between text prompt embeddings and generated image embeddings, typically using a different CLIP variant or resolution than standard CLIP Score. Higher is better.

## Input / output format

**Input**: Text prompt. (Baselines also condition on a single-view image generated by PixArt).

**Output**: 4-view images arranged in a 2x2 grid, or 9 rendered views of reconstructed 3D objects.

## Scoring recipe

```python
def evaluate(predictions, prompts, gt_images):
    clip_scores = []
    for img, txt in zip(predictions, prompts):
        img_emb = clip.encode_image(img)
        txt_emb = clip.encode_text(txt)
        clip_scores.append(cosine_similarity(img_emb, txt_emb))
    clip_score = mean(clip_scores)
    fid = frechet_distance(clip_features(predictions), clip_features(gt_images))
    return {'clip_score': clip_score, 'fid': fid}
```

## Common pitfalls

- FID is computed against a synthetic distribution of CAD-style images rather than real 3D object renders, which may not reflect true 3D quality.
- CLIP scores are highly dependent on the specific CLIP model variant (L/14 vs bigG) and prompt phrasing.
- View consistency is not measured directly but inferred from the FID of reconstructed 3D objects.

## Evidence (verbatim from paper)

> We primarily benchmark the quantitative results of our approach and other methods from two main dimensions: 1). Image-text alignment measured by CLIP score and CLIP-R score indicating the prompt follow ability of text-to-multi-view (T2MV) diffusion model. 2). Quality of generated images measured by FID(Heusel et al., 2017). Given the trend of decoupling multi-view image generation and sparse view reconstruction, we conduct tests separately on multi-view images by T2MV and rerendered images from generated 3D objects.

## Citation

```bibtex
@misc{sun2024bootstrap3d,
  title={Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data},
  author={Zeyi Sun et al. (2024)},
  year={2024},
  note={arXiv:2406.00093}
}
```

- arXiv: 2406.00093