Multicom Bench Eval

---
name: multicom-bench-eval
description: Evaluates a model's ability to compose multiple source images into a single coherent output while following textual instructions, maintaining image quality, and preserving facial consistency in human-object interaction scenarios. Use when the user wants to benchmark on MultiCom-Bench, or asks about evaluating this task. Reports VIEScore.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2601.15664
  bibtex_key: [REDACTED]
  confidence: high
---

# multicom-bench-eval

> Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling — Wei et al. (2026) (arXiv:2601.15664, 2026)

## What this evaluates

Evaluates a model's ability to compose multiple source images into a single coherent output while following textual instructions, maintaining image quality, and preserving facial consistency in human-object interaction scenarios.

## Datasets

- **MultiCom-Bench** — total 200; splits: test (200)

## Metrics

- `VIEScore` **(primary)** — range: [0, 1]
  - Composite score derived from VIEScore evaluation templates, measuring adherence to composition instructions, image quality, and facial consistency. Scores are averaged across dimensions and normalized to [0, 1].

## Input / output format

**Input**: A textual composition instruction/prompt and 2 to 6 source images.

**Output**: A single generated image.

## Scoring recipe

```python
def compute_viescore(predictions, prompts, gold_images):
    total_score = 0.0
    for pred, prompt, gold in zip(predictions, prompts, gold_images):
        s_adherence = viescore_template_adherence(pred, prompt)
        s_quality = viescore_template_quality(pred)
        s_consistency = viescore_template_consistency(pred, gold)
        total_score += mean([s_adherence, s_quality, s_consistency])
    return total_score / len(predictions)
```

## Common pitfalls

- Model performance degrades significantly when extending to 4–6 input images due to sequence modeling limits.
- Evaluation relies on VIEScore AI templates that assess multiple dimensions, requiring careful prompt engineering to avoid scoring bias.
- The benchmark is specifically curated for HOI scenarios, so results may not generalize to non-human-object compositions.

## Evidence (verbatim from paper)

> Recognizing the absence of standardized evaluation protocols for multi-image composition, we construct MultiCom-Bench, a carefully curated benchmark comprising 200 high-quality triplets specifically targeting HOI scenarios. Following VIEScore, we have designed stable and effective evaluation templates that assess model-generated results in multiple dimensions, including the adherence to composition instructions, image quality, and facial consistency.

## Citation

```bibtex
@misc{wei2026skyworkunipic3,
  title={Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling},
  author={Wei et al. (2026)},
  year={2026},
  note={arXiv:2601.15664}
}
```

- arXiv: 2601.15664