3D Shape Generation

Comprises 11 hand gesture categories from 29 subjects under 3 illumination conditions. Source: A Low Power, Fully Event-Based Gesture Recognition System

BEAT has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with \textit{facial expressions}, \textit{emotions}, and \textit{semantics}, in addition to the known correlation with \textit{audio}, \textit{text}, and \textit{speaker identity}. Based on this observation, we propose a baseline model, \textbf{Ca}scaded \textbf{M}otion \textbf{N}etwork \textbf{(CaMN)}, which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (\textbf{SRGR}). Qualitative and quantitative experiments demonstrate metrics' validness, ground truth data quality, and baseline's state-of-the-art performance. To the best of our knowledge, BEAT is the largest motion capture dataset for investigating human gestures, which may contribute to a number of different research fields, including controllable gesture synthesis, cross-modality analysis, and emotional gesture recognition. The data, code and model are available on \url{https://pantomatrix.github.io/BEAT/}.

We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new mesh-level holistic co-speech dataset. BEAT2 combines MoShed SMPLX body with FLAME head parameters and further refines the modeling of head, neck, and finger movements, offering a community-standardized, high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer, facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover, EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enhance the results' fidelity and diversity. Experiments demonstrate that EMAGE generates holistic gestures with state-of-the-art performance and is flexible in accepting predefined spatial-temporal gesture inputs, generating complete, audio-synchronized results.

Co-speech gestures are everywhere. People make gestures when they chat with others, give a public speech, talk on a phone, and even think aloud. Despite this ubiquity, there are not many datasets available. The main reason is that it is expensive to recruit actors/actresses and track precise body motions. There are a few datasets available (e.g., MSP AVATAR [17] and Personality Dyads Corpus [18]), but their sizes are limited to less than 3 h, and they lack diversity in speech content and speakers. The gestures also could be unnatural owing to inconvenient body tracking suits and acting in a lab environment. Thus, we collected a new dataset of co-speech gestures: the TED Gesture Dataset. TED is a conference where people share their ideas from a stage, and recordings of these talks are available online. Using TED talks has the following advantages compared to the existing datasets: • Large enough to learn the mapping from speech to gestures. The number of videos continues to grow. • Various speech content and speakers. There are thousands of unique speakers, and they talk about their own ideas and stories. • The speeches are well prepared, so we expect that the speakers use proper hand gestures. • Favorable for automation of data collection and annotation. All talks come with transcripts, and flat background and steady shots make extracting human poses with computer vision technology easier.

This is a 16.2-million frame (50-hour) multimodal dataset of two-person face-to-face spontaneous conversations. This dataset features synchronized body and finger motion as well as audio data. It represents the largest motion capture and audio dataset of natural conversations to date. The statistical analysis verifies strong intraperson and interperson covariance of arm, hand, and speech features, potentially enabling new directions on data-driven social behavior analysis, prediction, and synthesis.

The BiGe corpus is comprised of 54.360 shots of interest extracted from TED and TEDx talks. All shots are tracked with fully 3d landmarks.

The combinatorial 3D shape dataset is composed of 406 instances of 14 classes. Specifically, each object in the dataset is considered equivalent to a sequence of primitive placement. Source: Combinatorial 3D Shape Generation via Sequential Assembly

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