Tolga Birdal, Nassir Navab, Tony Wang, Lennart Bastian
We lifted 3 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| wngTn/DisPOSE | — | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| _extract_1d | Ran | wngTn/DisPOSE/src/models/assignment/sampler.py code served (permissive licence) · get_code("840e5c9d37be6a6b") |
| ProjectedConstrainedDDIM | Not yet run | wngTn/DisPOSE/src/models/assignment/sampler.py code served (permissive licence) · get_code("7461abad4c33f1fa") |
| _cosine_alpha_bar | Not yet run | wngTn/DisPOSE/src/models/assignment/sampler.py code served (permissive licence) · get_code("1eb4d1aeeeb8a945") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. We present DISPOSE, a novel pose estimation framework that models the discrete problem of associating individuals from multiple camera views as a generative process. By diffusing over the space of polystochastic tensors, DISPOSE learns to recover accurate 3D human associations without requiring 3D ground-truth supervision. As visualized in the trajectory (left to right), the diffusion process progressively resolves ambiguity, evolving from noise into sharp, consistent multi-view associations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.07419")
get_code_for_paper("2606.07419")
have("2606.07419")
Connect an agent — have() is free.