Causal BootsTAPIR: 256px, three query points
Official Google causal BootsTAPIR weights converted in GitHub Actions into encoder, query and recurrent-step ONNX graphs.
This is point tracking from three supplied initial image coordinates, not semantic foot detection, 3D reconstruction, or independent toe articulation.
Source code: https://github.com/google-deepmind/tapnet/tree/730cda1c730877cfedbe01bf87fb1cadb78a565d
Source weights: https://huggingface.co/google/tapnet/resolve/5d3fb48e76c5422841e38501514121e251beabb7/causal_bootstapir_checkpoint.pt
Source SHA-256: 87c1e752cf5ce56e3e2f7da460aeb4d40fc826d04ef2939bade86a5c7495377f.
Input: RGB float32 [1,1,256,256,3] in [-1,1]; first-frame queries [1,3,2] in model-pixel (y,x) order. Outputs are xy pixel tracks and two uncertainty logits.
Run encoder then query once; run encoder and step for each frame, retaining query features and feeding new_causal_state into the next causal_state. Start from the zero state defined by tapir-models.json.
The first-frame query extraction uses a mathematically equivalent 2D sample because time is fixed at zero. It is checked against the official 5D implementation.
The Actions comparison uses only a public CC BY image with artificial motion/occlusion. It verifies conversion numerics, not real-foot tracking quality or occlusion recovery accuracy.
WebGPU operator coverage is checked statically against ONNX Runtime Web 1.22.0; GPU execution, latency and RTX memory use are not measured by the CPU comparison.
No private video, extracted private frame, avatar file, or private pose trace is included.
License: Apache-2.0. Copyright and source notices are retained in LICENSE and the pinned source links.