| --- |
| license: mit |
| task_categories: |
| - other |
| tags: |
| - motion-generation |
| - diffusion-models |
| - cross-skeleton |
| - retargeting |
| - anonymous-submission |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # anytop-checkpoints |
|
|
| Pre-trained checkpoints accompanying the anonymous NeurIPS 2026 submission |
| **"Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of |
| Generative Motion Models"**. |
|
|
| This repository contains a single tarball, `anytop-checkpoints.tar.gz` |
| (6.5 GB compressed, 7.2 GB extracted), bundling the trained model weights |
| for the 14-method comparison reported in the paper. |
|
|
| ## Contents |
|
|
| After extraction (`tar -xzvf anytop-checkpoints.tar.gz`): |
|
|
| ``` |
| anytop-checkpoints/ |
| README.md (per-method usage notes) |
| anytop_v5/ AnyTop transductive (model000175000.pt, 34 MB) |
| ace_primary_70/ ACE-T transductive (ckpt_final.pt, 83 MB) |
| ace_inductive_60/ ACE-I inductive (ckpt_final.pt, 83 MB) |
| moreflow_primary_70/ MoReFlow-T (ckpt_final.pt, 79 MB) |
| moreflow_inductive_60/ MoReFlow-I (ckpt_final.pt, 79 MB) |
| anchor_label_flow/ AL-Flow (ckpt_final.pt, 86 MB) |
| anchor_label_flow_src/ AL-Flow-Src (ckpt_final.pt, 88 MB) |
| anchor_label_flow_src_g/ AL-Flow-Src-G (ckpt_final.pt, 87 MB) |
| dpg_sb_v3/ DPG-SB-v3 (final.pt 62 MB + z_stats.pt) |
| moreflow_vqvae/ 71 per-skeleton VQ-VAE encoders (~91 MB each) |
| Required for ACE / MoReFlow / DPG-SB-v3 inference |
| moreflow_caches/ Pre-extracted MoReFlow latents |
| (only required to retrain DPG-SB-v3 from scratch) |
| ``` |
|
|
| ## Verification |
|
|
| ``` |
| SHA-256: fc15f536510014a7bb953aa62cd24358de6af11f0d92f0f8360e2ec44b8251e2 |
| ``` |
|
|
| ```bash |
| sha256sum -c <<< "fc15f536510014a7bb953aa62cd24358de6af11f0d92f0f8360e2ec44b8251e2 anytop-checkpoints.tar.gz" |
| ``` |
|
|
| ## How to use |
|
|
| The companion code repository contains training scripts, evaluation runners, |
| and the SIF metric implementation. After downloading and extracting this |
| tarball into the code repo's `save/` directory, every per-method invocation |
| in the code repo's `REPRODUCE.md` § 4 will work without retraining. |
|
|
| ```bash |
| # From the code repo root: |
| mkdir -p save |
| tar -xzvf anytop-checkpoints.tar.gz |
| mv anytop-checkpoints/* save/ |
| rmdir anytop-checkpoints |
| |
| # Verify with one method: |
| python -m eval.baselines.run_anytop_v5 \ |
| --ckpt save/anytop_v5/model000175000.pt \ |
| --manifest eval/benchmark_v3/queries_sif/manifest.json \ |
| --output_dir results/anytop_sif |
| ``` |
|
|
| ## License |
|
|
| MIT, matching the upstream AnyTop release. |
|
|
| ## Anonymity |
|
|
| Every `args.json` file in the package has been swept for personal paths, |
| author names, and W&B entity strings. Checkpoint binaries (`*.pt`) contain |
| only PyTorch model and optimiser state. |
|
|
