HumanAct12 is a new 3D human motion dataset adopted from the polar image and 3D pose dataset PHSPD, with proper temporal cropping and action annotating. Statistically, there are 1191 3D motion clips(and 90,099 poses in total) which are categorized into 12 action classes, and 34 fine-grained sub-classes. The action types includes daily actions such as walk, run, sit down, jump up, warm up, etc. Fine-grained action types contain more specific information like Warm up by bowing left side, Warm up by pressing left leg, etc. Source: Action2Motion: Conditioned Generation of 3D Human Motions
To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment. The data includes all players messages and votes including timestamps, game management messages (e.g., announcements of the beginning and end of phases), in addition to records related to the agent, such as the prompts that were provided at each timestamp. While prior datasets for Mafia exist, ours is the first to integrate an LLM agent, allowing analysis of human-LLM interactions. Detailed analysis of the data is available in our paper: Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
Two4Two is a library to create synthetic image data crafted for human evaluations of interpretable ML approaches (esp. image classification). The synthetic images show two abstract animals: Peaky (arms inwards) and Stretchy (arms outwards). They are similar-looking, abstract animals, made of eight blocks. The core functionality of this library is that one can correlate different parameters with an animal type to create bias in the data.