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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.
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