Beining Han, Jia Deng, Yihan Wang, Kaiyu Yang, Ankit Goyal, Hei Law, Alejandro Newell, Lahav Lipson, Karhan Kayan, Zeyu Ma, Hongyu Wen, Yiming Zuo, and 3 more
We lifted 1 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 |
|---|---|---|
| princeton-vl/infinigen | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| Generator | Ran | princeton-vl/infinigen/src/infinigen/core/generator.py code served (permissive licence) · get_code("040576022e5d224f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond. Please visit infinigen.org for videos, code and pre-generated data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.09310")
get_code_for_paper("2306.09310")
have("2306.09310")
Connect an agent — have() is free.