Skeleton_Free_Motion_Transfer_Aayushi_Bhatiya_250272297

Skeleton-Free Motion Transfer — checkpoints

MSc dissertation project, Queen Mary University of London. Transfers human motion capture (AMASS) onto unrigged animal meshes (DeformingThings4D) with no skeleton, no rig, and no paired training data — predicts a per-face Jacobian field, integrated by a differentiable Poisson solve.

Full write-up, all figures and tables, and the actual reported numbers: see report_v6.docx / the results-overview notebook in the project repository. This model card summarizes; it is not the source of truth.

⚠️ License — read before using

Research and non-commercial use only. These weights were trained in part on AMASS, which is distributed under an academic license restricting use to non-commercial scientific research. Trained models inherit the most restrictive component license among their training data (AMASS, DeformingThings4D). By downloading these checkpoints you agree to:

• Use them for research/non-commercial purposes only.
• Not attempt to reconstruct the underlying AMASS or DeformingThings4D data from these weights.
• Cite the original AMASS, DeformingThings4D, and Neural Jacobian Fields papers (see Citation below) alongside this work.

If your use case is commercial or you are unsure whether it's covered, check the AMASS license terms directly before proceeding — this summary is not a substitute for that agreement.

Headline result — read this before trusting any single number

The project's central, best-supported finding is methodological, not a model claim: the geometric rigidity metrics conventionally used to score this kind of model (ARAP, smoothness) are confounded with how much the mesh moves — a model that barely moves scores near-perfectly on them by construction. Once displacement is controlled for, the ablation's headline result (removing the perceptual loss "improves" rigidity, p=0.016) is withdrawn — the two conditions occupy non-overlapping ranges of motion, so the comparison is not identifiable. Full detail: report_v6.docx Sections IV–V.

What is established: a working skeleton-free deformation substrate; a genuine accuracy/rigidity trade-off when the ARAP term is removed (2.28x, p<0.001); classical-ARAP metric parity (not improvement) against a reproduced SfPT baseline; a diagnosed and partially fixed geometric inversion-collapse failure mode.

What is not established: that the proposed perceptual loss (LVDP) improves transfer quality — this is reported as a null result, not a positive one.

Checkpoint inventory

All checkpoints are PyTorch state dicts: {"step", "model", "optimizer", "scheduler", "best_val", "cfg"} (encoder checkpoints have a different, simpler schema — see below). Load with torch.load(path, map_location="cpu", weights_only=False); the code to actually run them (MotionTransferModel, in src/models/pipeline.py) is in the project repository, not included in this weights-only release.

If you only want one checkpoint: fixAB_continue_s1337/step_800.pt is the one the reported numbers and qualitative results in report_v6.docx actually come from.

Data (not included, license-gated)

Running these checkpoints requires your own access to:

• AMASS (source motion)
• SMPL-H (body model, for source rendering)
• DeformingThings4D (target meshes)

None are redistributed here or in the project repository, per their own license terms.

Citation

If you use these weights, please cite the original datasets/methods this project builds on, alongside this dissertation:

Mahmood, N. et al. (2019). AMASS: Archive of Motion Capture as Surface Shapes. ICCV.
Li, Y. et al. (2021). 4DComplete: Non-rigid Motion Estimation Beyond the Observable Surface [DeformingThings4D]. ICCV.
Aigerman, N. et al. (2022). Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes. ACM TOG (SIGGRAPH).
Oquab, M. et al. (2023). DINOv2: Learning Robust Visual Features without Supervision. arXiv:2304.07193.

Reproducibility

Exact seeds (42, 1337), config, and evaluation protocol for every checkpoint above are in report_v6.docx Table IV and the project repository's START_HERE.md. python -m pytest -q (126 passed, 2 skipped) verifies the codebase these checkpoints were produced by.