Add animal pipeline support to FMPose3DInference (and add test suite)

[deruyter92]

Summary

• Extend FMPose3DInference with full animal (26-joint Animal3D) pipeline support,
  including SuperAnimalEstimator (DeepLabCut 2D), AnimalPostProcessor (limb
  regularisation), and the for_animals() convenience constructor.
• Add a basic test suite (tests/test_fmpose3d.py) covering all public and
  internal components of the inference API with 78 tests (64 unit + 14 functional).

Changes

fmpose3d/fmpose3d.py

• Add SuperAnimalEstimator: wraps DeepLabCut SuperAnimal, maps quadruped80K keypoints
  to the 26-joint Animal3D layout via _map_keypoints, handles missing detections
  (zero-fill fallback).
• Add AnimalPostProcessor: applies limb regularisation (Rodrigues rotation aligning
  average limb direction to vertical) instead of root-zeroing / camera_to_world.
• Add compute_limb_regularization_matrix and apply_limb_regularization helpers.
• Add _default_components resolver that selects the correct estimator + postprocessor
  based on model_type.
• Add FMPose3DInference.for_animals() class method for one-line animal pipeline setup.

tests/test_fmpose3d.py (new)

• Unit tests (64): _map_keypoints (direct mapping, interpolation, out-of-range,
  multi-individual), limb regularisation (identity, rotation, 180-degree, degenerate,
  valid-rotation properties), both postprocessors (shape, mutation, camera_rotation
  handling, all-zero pose), _default_components, FMPose3DInference construction
  (for_animals, custom injection, _resolve_pad), _ingest_input (arrays, paths,
  directories, 8 error cases), _load_weights error paths, pose_3d input validation
  (wrong ndim, zero frames, 4D→first person, TTA path, progress callback, seed
  reproducibility), dataclasses, mocked SuperAnimalEstimator.predict (no-detection
  and valid-detection branches).
• Functional tests (14): full human pipeline (HRNet 2D → FMPose3D 3D, shape checks,
  root-zeroed, world z-floor, finite, seed reproducibility) and full animal pipeline
  (DLC 2D → FMPose3D-animals 3D, shape checks, finite, magnitude sanity,
  reproducibility). Marked @pytest.mark.functional and auto-skipped when pretrained
  weights are not present.

pyproject.toml

• Register functional pytest marker.

[C-Achard]

Looks good ! Should be easy to use downstream, definitely great there is some testing.

Some minor comments for future usage but nothing major.