l-k-r/reconstructed_motions

Dataset Layout

This dataset has a tree structure layout to classify motions.

.
├── Root 
│   ├── Layer 1
|       ├── ...
|           ├── Layer N # all the nested motions under the Layer N will use the object mesh
|               ├── object
|               ├── Layer N+1 
|               ├── ...
│   ├── Layer 1

Reconstructed Human–Object Motions

3D reconstructions of HOI data, recovered from monocular RGB video. Each sequence provides the human motion (SMPL‑H parameters) and the object motion (the cube's 6‑DoF trajectory), both in the same metric, z‑up world frame, plus a rendered preview video. The shared object geometry (a textured cube mesh) is stored once per collection.

object/ — the cube mesh

object.obj is the ground-truth object used for every sequence in the collection: a textured cube, ~0.71 m per side, centered at the local origin (vertices span [-0.355, +0.355] on each axis). Load it once and reuse it for all sequences; the per-frame world placement comes from each sequence's object_motion.npz.

motion.npz — human motion (SMPL‑H)

Per-frame SMPL‑H body parameters. FK'ing these with the SMPL‑H body model reproduces the human mesh exactly as shown in motion.mp4.

key	shape	dtype	meaning
betas	(T, 10)	float32	SMPL‑H shape coefficients (constant across frames)
poses	(T, 156)	float32	full axis-angle pose = global_orient(3) + body_pose(63) + left_hand_pose(45) + right_hand_pose(45)
global_orient	(T, 3)	float32	root orientation (axis-angle), world frame
body_pose	(T, 63)	float32	21 body-joint rotations (axis-angle)
left_hand_pose	(T, 45)	float32	15 left-finger rotations (axis-angle, not PCA)
right_hand_pose	(T, 45)	float32	15 right-finger rotations (axis-angle, not PCA)
transl	(T, 3)	float32	root translation (meters), world frame
gender	scalar str	<U4	"male" / "female"
model_type	scalar str	<U5	"smplh"
num_betas	scalar int	int64	10
fps	scalar int	int64	30
frames	(T,) str	<U6	source frame ids of the input video

Notes:

• global_orient and transl already include the world/ground alignment (see Coordinate frame) — no extra transform is needed; FK gives world-frame joints and vertices directly.
• poses is redundant with the four split fields; use whichever is convenient.
• Body model = SMPL‑H, neutral hand PCA off (use_pca=False), 10 shape betas.

object_motion.npz — object (cube) motion

The cube's rigid 6‑DoF pose per frame, in the same world frame as motion.npz.

key	shape	dtype	meaning
obj_pos	(T, 3)	float64	cube center position (meters), world frame
obj_rot	(T, 4)	float64	cube orientation as a unit quaternion, wxyz (scalar-first)

To place the cube at frame t: rotate the local object.obj vertices by obj_rot[t] and translate by obj_pos[t]: V_world = R(obj_rot[t]) @ V_local + obj_pos[t].

Coordinate frame & conventions

• World frame: right-handed, z-up, meters.
• Grounded: the scene rests on the ground plane z = 0 (the globally lowest point of the human/cube sits at z = 0); "up" has been corrected to +z.
• Human and object are consistent: same frame, same length, same fps — so their contact/interaction is preserved frame-by-frame.
• Quaternions in object_motion.npz are wxyz (scalar-first). SMPL‑H rotations in motion.npz are axis-angle.

motion.mp4

A 30‑fps preview rendering the SMPL‑H human mesh together with the posed cube, from an orbit camera. It visualizes exactly the data in the two .npz files.

Loading & usage (Python)

import numpy as np, torch, trimesh, smplx
from scipy.spatial.transform import Rotation as Rot

seq = "LargeCubeFlip/SingleHandFingersSideFlip/Date01_Sub01_cube_SingleHandFingersSideFlip_cam0"

# --- human (SMPL-H) ---
m = np.load(f"{seq}/motion.npz", allow_pickle=True)
T = len(m["poses"])
model = smplx.create("<path/to/body_models>", model_type="smplh",
                     gender=str(m["gender"]), use_pca=False, num_betas=int(m["num_betas"]),
                     batch_size=T)
out = model(betas=torch.tensor(m["betas"]),
            global_orient=torch.tensor(m["global_orient"]),
            body_pose=torch.tensor(m["body_pose"]),
            left_hand_pose=torch.tensor(m["left_hand_pose"]),
            right_hand_pose=torch.tensor(m["right_hand_pose"]),
            transl=torch.tensor(m["transl"]))
human_verts = out.vertices.detach().numpy()      # (T, 6890, 3), world frame (z-up, meters)

# --- object (cube) ---
o = np.load(f"{seq}/object_motion.npz")
cube = trimesh.load("LargeCubeFlip/object/object.obj", force="mesh")
Vloc = np.asarray(cube.vertices)                  # (Nv, 3), centered
q_xyzw = o["obj_rot"][:, [1, 2, 3, 0]]            # wxyz -> xyzw for scipy
obj_verts = np.einsum("tij,vj->tvi", Rot.from_quat(q_xyzw).as_matrix(), Vloc) + o["obj_pos"][:, None]
# obj_verts[t] is the cube in the world at frame t, aligned with human_verts[t]

(You need the SMPL‑H body model files from https://mano.is.tue.mpg.de / SMPL‑X to instantiate smplx.create; they are not redistributed here.)