MINOR_REVISION.md

AgentHOI: minor-revision evaluation materials

Download 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. 9–10; 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/	Final figure PDFs 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:

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.

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. 10 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 has 84% top-1 accuracy, the base Wan2.1-T2V-1.3B has 14%, and chance is 25%. The model is used for feature alignment during training and removed at inference.

results/all_cases_comparison.csv stores measured per-case ranks, losses and top-1 decisions; 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.