Add dataset card

README.md

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+---
+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.