Blame for MINOR_REVISION.md

# AgentHOI: minor-revision evaluation materials

Download [agenthoi_minor_revision.zip](agenthoi_minor_revision.zip) and extract it to obtain the `minor_revision/` directory.


This release accompanies the additional evaluations in the TOG minor revision of
**AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via
Implicit Representation Alignment**.

## Contents

| Directory | Contents | Paper reference |
|---|---|---|
| `plan_evaluation/` | Plans under single-agent, w/o clipping refiner and full settings; blinded Qwen3-VL annotations; metric outputs and judge scripts | Table 3; supplementary D |
| `agent_ablation/` | Keyboard, cream tube, razor and mask inputs, stage outputs and generated videos for full, w/o Perception and w/o Interaction | Fig. 8–9; supplementary Fig. 2–3 |
| `motion_text_matching/` | Candidate descriptions, per-case matching results, aggregate results and example RGB/motion frames | Table 4; supplementary Fig. 4 |
| `paper_figures/`, `paper_tables/` | 1107 experiment figures, captions and table sources | Same numbering as the revised paper |

The fifth and sixth ablation examples from the initial experiment export are
excluded. Figures use the final captions and layouts, including the cream-tube
hand-transfer example. `M_motion` is the training-only auxiliary motion model.

## Plan evaluation

The judge is **Qwen3-VL-32B-Instruct**, independent of the
**InternVL3.5-38B** planner family. It receives the human reference, object
reference, input caption and timeline without the setting label, with greedy
decoding. Each timeline has one-second segments.

The reported mechanism metrics are object grounding, explicit acting-hand
assignment over contact segments, plan-level hand-state consistency, and the
fraction of segments containing more than two motions. Metrics are computed per
plan and then averaged. Plans without contact are excluded from the acting-hand
metric. Whole-plan body-contact staging and one-step attachment are assessed in
a separate pass (`results/contact/`); the preliminary per-segment body-contact
labels in `results/mechanism/` are not the paper's body-contact audit.

The decomposition improves object grounding and hand specification. The clipping
refiner turns abrupt body attachment into staged contact. Some refined phrases
leave the acting hand implicit; the video ablation in the paper shows unchanged
InternVL-H and improved TVA and InternVL-inter relative to w/o clipping refiner.

### Inspect or reproduce the recorded metrics

From the extracted `minor_revision` directory:

```bash
python scripts/summarize_release.py
python scripts/verify_release.py
```

`summarize_release.py` recomputes the paper's plan metrics directly from the
released judge annotations and the motion matching accuracy from per-case rows.
The original aggregate JSON files also retain additional diagnostic metrics.

### Rerun the plan judge

Download the reference images already provided in this dataset repository:
`benchmarks/assets/<case-id>/ref_human.png` and `ref_object.png`. Download the
local Qwen3-VL-32B-Instruct checkpoint and install a Transformers version that
provides `Qwen3VLForConditionalGeneration`, PyTorch and Pillow. The original
experiment used multiple GPUs; the release supports configurable GPU selection.

```bash
export AGENTHOI_EVAL_ROOT=/absolute/path/to/agenthoi-eval
export PLAN_JUDGE_MODEL=/absolute/path/to/Qwen3-VL-32B-Instruct
export PLAN_JUDGE_GPUS=0,1,2,3,4,5,6,7
export PLAN_OUTPUT_DIR=/absolute/path/to/new_judge_results
python plan_evaluation/scripts/run_mechanism_qwen.py
python plan_evaluation/scripts/mechanism_metrics.py
python plan_evaluation/scripts/run_contact_qwen.py
```

`plans.jsonl` image paths are relative to `AGENTHOI_EVAL_ROOT`. Original variant
strings are preserved (`Single Agent (prior)`, `w/o Refiner (prior)`,
`Full (prior)`); `(prior)` is an export label. The judge never sees that label.
Rerunning inference requires the GPU/model dependencies above. Only data
aggregation and release integrity checks were executed during packaging.

## Agent ablations and intermediate outputs

`manifest.json` maps the four cases to reference images and captions.
`stage_traces.json` and `plans/matched_cuda_raw/` preserve the structured
outputs, including original agent prompts. `videos/` contains the corresponding
full-control, no-perception and no-interaction generations.

The keyboard and mask cases illustrate incorrect object categories or
unrequested wearing after removing an agent. Fig. 9 uses the cream tube:
the refiner inserts alignment with the receiving hand before transfer.
The cream-tube video variants show smaller differences than the failure cases.

## Motion–text matching

The test pairs each ground-truth HOMA motion sequence with its correct
interaction description and three incorrect actions on the same object.
The incorrect actions use the same templates: throwing/catching, putting on
the floor/kicking, and lifting overhead/swinging.

All four candidates receive the same Gaussian noise at noise level 0.5
(timestep 500); the candidate with the lowest flow-matching loss is selected.
`M_motion` on skeleton-and-mask inputs has 84% top-1 accuracy and mean loss gap
+5.493168222407501e-4. The base Wan2.1-T2V-1.3B on RGB inputs has 14% accuracy
and mean loss gap -6.11583391825328e-6; chance is 25%. The model is used for feature alignment during training and removed at
inference.

`results/all_cases_comparison.csv` stores the current RGB-baseline comparison;
`results/candidate_losses.json` contains all four measured candidate losses;
`results/summary.json` stores the recomputed aggregates; `data/candidates.json` retains descriptions and encoded-input
hashes. Fields such as `input` and `source_video` preserve the original
experiment's provenance and are not portable file locations.
Example RGB and motion frames are bundled. Full encoded motion latents,
the trained `M_motion` checkpoint and its custom inference implementation are
not included in the supplied materials, so this release supports reaggregation
of the recorded matching results rather than a standalone model-inference rerun.

## Integrity and provenance

`SHA256SUMS` records every released file except itself. `PROVENANCE.json`
records the source-archive hashes and packaging transformations.
The existing dataset's benchmark images and GT videos remain the source inputs.


## 2026-11-07 update

The release now uses the measured native RGB baseline run for Table 4.
The earlier baseline on skeleton-and-mask inputs is retained only as
`legacy_base_motion_comparison.csv` and `legacy_base_motion_aggregate.csv`.
Current results and the example figure use the RGB run. Source protocol records
are retained under `motion_text_matching/source_protocols/`.

Figures and tables are synchronized with `TOG2026_hzy_AgentHOI_1107`.
The package additionally includes the real-input comparison (Fig. 4), agent
ablation, implicit-alignment, data, VLM and agent-transfer experiment figures.
For these existing paper experiments, supplied figures and tables are included;
no additional per-video metric outputs or inference reruns are claimed.
The original plan judgments, stage traces and 12 agent-ablation videos are unchanged.