# AgentHOI evaluation

Benchmarks, four-metric runners, and per-baseline comparison launchers used for
the AgentHOI paper.

## Layout

```
evaluation/
├── README.md                 # this file
├── env.example.sh            # copy -> env.local.sh and fill paths
├── benchmarks/
│   ├── datasets_portable/    # portable test JSONs (relative paths only)
│   └── manifests/            # per-metric manifests (Object-DINO / TVA)
├── scripts/
│   ├── prepare_eval_jsons.{py,sh}
│   ├── prepare_baselines_jsons.sh
│   ├── prepare_benchmark_assets.{py,sh}
│   ├── absolutize_dataset_json.py    # turn portable JSON into absolute-path JSON
│   ├── eval_common.sh                # shared helpers (no internal paths)
│   ├── run_all_metrics.sh            # 4 metrics in one pass
│   ├── run_eval.sh                   # one-liner: bench + method
│   ├── aggregate_results.{py,sh}
│   ├── copy_eval_videos.sh           # stage videos under videos/<bench>/<method>/
│   ├── package_hf_upload.sh          # tarball assets/results for HF dataset upload
│   ├── unpack_hf_download.sh         # inverse of the above
│   └── setup_deps.sh                 # symlink the metric implementations repo
├── baselines/                        # per-method comparison launchers
├── docs/                             # metric notes
├── videos/                           # populated by you / copy_eval_videos.sh
└── results/                          # written by run_all_metrics.sh
```

## Metrics (4 suites)

| # | Metric                              | Conda env (suggested) | Output                  |
|---|-------------------------------------|-----------------------|-------------------------|
| 1 | **Object-DINO**                     | `dino_obj`            | `obj_dino_result.json`  |
| 2 | **VBench**                          | `vbench`              | `vbench_results/`       |
| 3 | **TVA** (VideoAlign / VideoReward)  | `videoreward`         | `TVA_result.json`       |
| 4 | **InternVL**                        | `internvl`            | `internvl/summary.json` |

VBench dimensions: `motion_smoothness`, `dynamic_degree`, `aesthetic_quality`,
`overall_consistency`. InternVL scores object consistency, human consistency,
and interaction quality.

## Prerequisites

1. **Metric implementation repo** — VBench / VideoAlign / Object-DINO / InternVL
   are not bundled (they have heavy weights and large codebases). Point
   `EVAL_DEPS_ROOT` at a local clone:

   ```bash
   bash scripts/setup_deps.sh /path/to/your/evaluations_repo
   export EVAL_DEPS_ROOT=/path/to/your/evaluations_repo
   ```

2. **Conda envs** (or set `PYTHON_*` in `env.local.sh`): one env per metric.

3. **Aux weights**: VideoReward / VideoAlign checkpoint, InternVL3.5-38B,
   VBench cache.

4. **Generated videos**: one `.mp4` per `image_id` in the benchmark JSON, under
   `videos/<benchmark>/<method>/`.

## Quick start

All paths are relative to `release/evaluation/`. The repo ships **one demo
case** (`benchmarks/datasets_portable/demo_crosstest.json`) using the bundled
`examples/I2V/` reference images, so you can sanity-check the pipeline on
a single sample before downloading the full benchmarks.

```bash
cd release/evaluation

# 1) Once: link metric code (VBench / VideoAlign / Object-DINO / InternVL)
bash scripts/setup_deps.sh /path/to/your/evaluations_repo

# 2) Optional: set custom PYTHON_* / weight paths
cp env.example.sh env.local.sh
# (edit env.local.sh)

# 3) Run inference for the demo case (from repo root):
#    cd .. && bash run_inference.sh --dataset_path evaluation/benchmarks/datasets_portable/demo_crosstest.json
#    -> writes outputs/demo/<image_id>.mp4
#
# 4) Stage the resulting mp4 under videos/<bench>/<method>/:
#    mkdir -p videos/crosstest/ours
#    cp ../outputs/demo/obj=img_0=human=img_44.mp4 videos/crosstest/ours/

# 5) Run the four metrics:
bash scripts/run_eval.sh crosstest ours
# equivalent: bash baselines/run_ours_crosstest.sh

# 6) Aggregate
bash scripts/aggregate_results.sh
# -> results/eval_summary.xlsx
```

To use your own video directory:

```bash
export VIDEO_PATH=/your/mp4/dir
bash scripts/run_eval.sh crosstest custom
```

## Benchmarks

Three test sets used in the paper. Datasets are NOT bundled (they reference
copyrighted source images and are too heavy for git); see
`benchmarks/datasets_portable/demo_crosstest.json` for the schema.

| Benchmark      | Suggested file                          | Notes                                   |
|----------------|-----------------------------------------|-----------------------------------------|
| **Cross-test** | `hoi_crosstest_<date>.json`             | Main paper comparison (72 clips)        |
| **AC**         | `hoi_AC_test_evalset.json`              | AnchorCrafter-aligned test set          |
| **HOMA**       | `hoi_selfcollect_homa1_evalset.json`    | In-the-wild self-collected set          |

Use the matching manifests (`obj_dino_<bench>.json`, `tva_<bench>.json`) and
the corresponding launcher under `baselines/`.

## Baseline comparison workflow

1. Run inference for **ours** and each baseline on the same benchmark JSON
   (the video generation step in the main README).
2. Stage each method's mp4 under `videos/<benchmark>/<method>/`, or set
   `VIDEO_PATH` per run.
3. Launch `baselines/run_<method>_<benchmark>.sh` (or
   `bash scripts/run_eval.sh <benchmark> <method>`).
4. `bash scripts/aggregate_results.sh` to build a comparison Excel.

Compared methods in the paper include VACE, HuMo, HOMA, AnchorCrafter; add a
new method by copying any `baselines/run_*.sh`.

## Notes

- **Paths in dataset JSON** (`ref_image_path`, ...) should be relative to the
  evaluation root or absolute on the running machine. Use
  `scripts/absolutize_dataset_json.py` to materialize an absolute-path JSON
  on a new host.
- **Large artifacts** (VBench weights, full benchmark videos, `results/`) are
  not bundled. Keep them under `EVAL_DEPS_ROOT` or download separately.

## Relation to training / inference

| Stage                                   | Repo path                                                   |
|-----------------------------------------|-------------------------------------------------------------|
| Inference (generate videos to evaluate) | `../run_inference.sh`                                       |
| Eval benchmark as inference JSON        | `benchmarks/datasets_portable/demo_crosstest.json` (sample) |

Evaluation does **not** load the HOI transformer; it only reads `.mp4` files.