We lifted 5 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| taldatech/ddlp | canonical | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| identity | Ran | taldatech/ddlp/modules/diffusion_modules.py code served (permissive licence) · get_code("7f1040f5e3991d5e") |
| conv1x1 | Ran | taldatech/ddlp/modules/modules.py code served (permissive licence) · get_code("d9def42110729a85") |
| default | Ran | taldatech/ddlp/modules/diffusion_modules.py code served (permissive licence) · get_code("d1ef6b8cb9a28a53") |
| exists | Ran | taldatech/ddlp/modules/diffusion_modules.py code served (permissive licence) · get_code("608e364a9d2376a3") |
| conv3x3 | Not yet run | taldatech/ddlp/modules/modules.py code served (permissive licence) · get_code("00be6a9dc697991e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a new object-centric video prediction algorithm based on the deep latent particle (DLP) representation. In comparison to existing slot- or patch-based representations, DLPs model the scene using a set of keypoints with learned parameters for properties such as position and size, and are both efficient and interpretable. Our method, deep dynamic latent particles (DDLP), yields state-of-the-art object-centric video prediction results on several challenging datasets. The interpretable nature of DDLP allows us to perform ``what-if'' generation -- predict the consequence of changing properties of objects in the initial frames, and DLP's compact structure enables efficient diffusion-based unconditional video generation. Videos, code and pre-trained models are available: https://taldatech.github.io/ddlp-web
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.05957")
get_code_for_paper("2306.05957")
have("2306.05957")
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