We lifted 4 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| openrobotlab/unihsi | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| farthest_point_sample | Ran | openrobotlab/unihsi/unihsi/env/tasks/unihsi_partnet.py pointer only (licence: NONE) · get_code("8016bf9f4fcc1ce4") |
| load_cfg | Ran | openrobotlab/unihsi/unihsi/utils/config.py pointer only (licence: NONE) · get_code("eb655c1f24399d8c") |
| set_seed | Ran | openrobotlab/unihsi/unihsi/utils/config.py pointer only (licence: NONE) · get_code("62d0cd8396969ece") |
| load_label | Not yet run | openrobotlab/unihsi/unihsi/env/tasks/unihsi_partnet.py pointer only (licence: NONE) · get_code("10c38dcd2d287224") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and the development of a user-friendly interface, require further exploration before the practical application of HSI. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands. This framework is built upon the definition of interaction as Chain of Contacts (CoC): steps of human joint-object part pairs, which is inspired by the strong correlation between interaction types and human-object contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To facilitate training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. The project page is at https://github.com/OpenRobotLab/UniHSI .
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.07918")
get_code_for_paper("2309.07918")
have("2309.07918")
Connect an agent — have() is free.