EgoH4RT: Bringing Egocentric Hands and Camera 4D Reconstruction Together

EgoH4RT Team

Quantitative Evaluation

End-to-end Scaling with Video Length

Raw MP4 to native predictions on one H100 80GB GPU with node-local SSD.

Combined scatter plot of end-to-end runtime and peak GPU memory at 128, 512, 1024, and 1802 source frames
Every point is the arithmetic mean of two measured runs after warm-up. Along each connected trajectory, points proceed from 128 to 512, 1,024, and 1,802 source frames. EgoH4RT, HaWoR, HandFlow and MINT output bilateral hands and camera motion; ACE-Ego-Hand outputs bilateral hands only.

Camera-space Bimanual Hand Reconstruction

Bimanual errors from the final HOT3D evaluation.

millimetres ↓
Dataset Method TA-MPJPE PA-MPJPE Abs-MPJPE Root Error
HOT3DMINT20.3938.23853.46556.956
WiLoR20.9497.05150.57444.051
HaWoR15.4656.25135.02141.083
HandFlow†12.7225.64221.91120.004
ACE-Ego-Hand11.3435.92119.38417.009
EgoH4RT9.3984.52416.05914.704

† HandFlow training includes 22/27 evaluated HOT3D recordings.

Long-range Camera Reconstruction on Xperience

Four held-out 235–297 s recordings with approximately 10 m trajectory diameter.

millimetres ↓
MethodATE-SE(3)ATE-Sim(3)
EgoH4RT310.193268.601
HaWoR1593.0241500.451
HandFlow2330.1692258.552
MINT2608.1522345.939
LingBot-Map1421.826724.688
ABot-Recon2371.2691873.781

Xperience's released metric SLAM supplies the camera reference for this evaluation.

Translation and rotation relative pose error over temporal horizons from one to 220 seconds on Xperience
Relative translation and rotation error over exact temporal gaps from 1 to 220 seconds; all four recordings are pooled by frame pair.