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Benchmarks, four-metric runners, and per-baseline comparison launchers used for
the AgentHOI paper.
## TOG minor-revision evaluations
Download [agenthoi_minor_revision.zip](agenthoi_minor_revision.zip) and see
[MINOR_REVISION.md](MINOR_REVISION.md) for the protocols and reproduction steps.
The archive contains:
- Generated plans and blinded Qwen3-VL annotations for single-agent, w/o clipping
refiner and full settings (Table 3).
- Reference images, stage outputs and videos for the four agent-ablation cases;
final Fig. 9–10 and supplementary Fig. 2–3.
- Motion–text matching candidate descriptions, per-case results and example
frames (Table 4 and supplementary Fig. 4).
- Final figure PDFs, table sources, metric aggregation scripts and checksums.
The plan metrics and motion–text top-1 results can be recomputed from the released
annotations and per-case records without running a model.
## 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.
|