| # 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. |
|
|
