The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
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.
# 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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