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Important Note on File Size & PyTorch Hub: Each STATERA checkpoint is roughly 1.25 GB. Because we unfroze the final two transformer blocks of the V-JEPA backbone during training to adapt its latent space to Newtonian physics, the base weights were physically modified. To ensure seamless out-of-the-box inference, our checkpoints save the entire integrated state dictionary (the full ViT-Large backbone + custom decoder).(Note: Your local inference script must still connect to PyTorch Hub to fetch Meta's underlying Python class definitions to build the architecture graph in memory before our modified 1.25 GB weights are loaded into it).
STATERA-50K-Crescent.pth--target_type crescent flag matching the rotational angle, which then decays to a high-frequency point).STATERA-50K-Sigma.pth--target_type dot flag alongside curriculum label smoothing) to cure the gravitational settling bias./ablations)/ablations folder for research reproducibility. These correspond directly to the baseline and ablation studies detailed in our paper (Section 4.4, Table 1 & Table 2, and Appendix E):STATERA-1K-DINOv2.pth (1.25 GB): Evaluates a purely spatial foundation model (DINOv2). Equipped with the exact same 1D temporal mixer and 2.5D decoder as STATERA. Proves that temporal convolutions applied post-extraction cannot recover lost intra-frame momentum, causing the model to collapse to the visual/geometric centroid.STATERA-1K-VideoMAE.pth (1.25 GB): Evaluates VideoMAE v2 as an alternative temporal foundation. Despite possessing identical 1D sequence mixing, it forces the network to memorize visual surface textures due to its pixel-level reconstruction objective, strongly suggesting that true kinematic extraction requires predictive latent physics (like V-JEPA) rather than just spatio-temporal attention.STATERA-1K-ResNet3D.pth (146 MB): Evaluates a standard 3D-CNN temporal baseline trained end-to-end. (Note the smaller file size, as it does not utilize the ViT-Large backbone). Lacks V-JEPA's latent tubelet priors, resulting in wild overshooting artifacts and poor physical disentanglement.STATERA-1K-No-Z-Depth.pth (1.25 GB): Ablates the 1D Z-Depth regularizer. Demonstrates that without absolute 3D depth supervision, the dynamically-cropped 2D network loses physical scale constraints causing predictions to severely overshoot the object's physical bounds, driving up Euclidean error.STATERA-1K-Frozen-Anchor.pth (1.25 GB): An architecture ablation where the final two transformer blocks of the V-JEPA backbone were kept strictly frozen. Performance significantly degrades, mirroring DINOv2 and proving the necessity to fine-tune the temporal representations specifically for kinematic tasks.STATERA-1K-Anchor.pth (1.25 GB): The standard low-data baseline trained on only 1,000 sequences to demonstrate spatial overfitting and temporal starvation.STATERA-1K-Standard-Sigma.pth (1.25 GB): Baseline target dynamics testing standard Gaussian smoothing without applying the variance-decay curriculum.STATERA-1K-Static-Dot.pth (1.25 GB): Baseline target dynamics testing a static, non-decaying coordinate dot, leading to severe gradient instability during continuous sub-pixel coordinate extraction.vjepa2_1_vit_large_384).--finetune_blocks 2 and gradient accumulation) to adapt the visual latent space to Newtonian mechanics.