We lifted 3 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| JunweiLiang/Multiverse | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_grid_input | Ran | JunweiLiang/Multiverse/code/multifuture_inference.py code served (permissive licence) · get_code("c395c68411a52c40") |
| load_traj | Ran | JunweiLiang/Multiverse/code/multifuture_inference.py code served (permissive licence) · get_code("88f4ff3988d20c7c") |
| get_inputs | Not yet run | JunweiLiang/Multiverse/code/multifuture_inference.py code served (permissive licence) · get_code("3c55e4863789ee69") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals. This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories. The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs. We refer to our model as Multiverse. We show that our model achieves the best results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which just contains one possible future).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1912.06445")
get_code_for_paper("1912.06445")
have("1912.06445")
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