tapnet

Tracking Any Point (TAP) — Model Checkpoints Official pre-trained checkpoints for the **Tracking Any Point (TAP)** family of models from Google DeepMind & Google Research. [GitHub] [TAP-Vid] [TAPIR] [RoboTAP] [Blog Post] [BootsTAP] [TAPVid-3D] [TAPNext] [TRAJAN] [TAPNext++] Model Overview The TAP model family addresses the task of **tracking any point on any solid physical surface** across video frames. Given a query point on one frame, the model predicts where that point moves to on every other frame, along with an occlusion/visibility estimate. | Model | Key Contribution | |:--|:--| | **TAP-Net** | Baseline model introduced with the TAP-Vid benchmark | | **TAPIR** | Two-stage matching + refinement; large accuracy gains over TAP-Net | | **BootsTAPIR** | TAPIR + bootstrapped self-training on unlabeled video | | **TAPNext / BootsTAPNext** | Next-token-prediction formulation; fastest and simplest tracker | | **TAPNext++** | Fine-tuned BootsTAPNext with 40× longer stable tracking, occlusion handling, and re-detection | | **TRAJAN** | Point trajectory autoencoder for motion representation learning | Checkpoints All checkpoints are released under the **Apache License 2.0**. TAPNext++ (latest) | File | Resolution | Framework | DAVIS First (AJ) | Kinetics First (AJ) | RoboTAP First (AJ) | |:--|:--:|:--:|:--:|:--:|:--:| | `tapnextpp_ckpt.pt` | 256×256 | PyTorch | 65.6% | 53.9% | 61.1% | | `tapnextpp_512.ckpt` | 512×512 | PyTorch | 67.0% | — | — | TAPNext / BootsTAPNext | File | Resolution | Framework | DAVIS First (AJ) | |:--|:--:|:--:|:--:| | `bootstapnext.pth` | 256×256 | PyTorch | 65.25% | | `bootstapnext_ckpt.npz` | 256×256 | Jax | 65.25% | TAPIR & BootsTAPIR | File | Mode | Resolution | Framework | DAVIS First (AJ) | |:--|:--:|:--:|:--:|:--:| | `tapir_checkpoint_panning.pt` | Offline | 256×256 | PyTorch | 58.5% | | `tapir_checkpoint_panning.npy` | Offline | 256×256 | Jax | 58.5% | | `causal_tapir_checkpoint.npy` | Online | 256×256 | Jax | 56.2% | | `bootstapir_checkpoi