SIMS: Simulating Human-Scene Interactions with Real World Script Planning

Simulating long-term human-scene interaction is a challenging yet fascinating task. Previous works have not effectively addressed the generation of long-term human scene interactions with detailed narratives for physics-based animation. This paper introduces a novel framework for the planning and controlling of long-horizon physical plausible human-scene interaction. On the one hand, films and shows with stylish human locomotions or interactions with scenes are abundantly available on the internet, providing a rich source of data for script planning. On the other hand, Large Language Models (LLMs) can understand and generate logical storylines. This motivates us to marry the two by using an LLM-based pipeline to extract scripts from videos, and then employ LLMs to imitate and create new scripts, capturing complex, time-series human behaviors and interactions with environments. By leveraging this, we utilize a dual-aware policy that achieves both language comprehension and scene understanding to guide character motions within contextual and spatial constraints. To facilitate training and evaluation, we contribute a comprehensive planning dataset containing diverse motion sequences extracted from real-world videos and expand them with large language models. We also collect and re-annotate motion clips from existing kinematic datasets to enable our policy learn diverse skills. Extensive experiments demonstrate the effectiveness of our framework in versatile task execution and its generalization ability to various scenarios, showing remarkably enhanced performance compared with existing methods. Our code and data will be publicly available soon.

Social Motion Prediction with Cognitive Hierarchies

Humans exhibit a remarkable capacity for anticipating the actions of others and planning their own actions accordingly. In this study, we strive to replicate this ability by addressing the social motion prediction problem. We introduce a new benchmark, a novel formulation, and a cognition-inspired framework. We present Wusi, a 3D multi-person motion dataset under the context of team sports, which features intense and strategic human interactions and diverse pose distributions. By reformulating the problem from a multi-agent reinforcement learning perspective, we incorporate behavioral cloning and generative adversarial imitation learning to boost learning efficiency and generalization. Furthermore, we take into account the cognitive aspects of the human social action planning process and develop a cognitive hierarchy framework to predict strategic human social interactions. We conduct comprehensive experiments to validate the effectiveness of our proposed dataset and approach. Code and data are available at https://walter0807.github.io/Social-CH/.

Rhythmic Gesticulator: Rhythm-Aware Co-Speech Gesture Synthesis with Hierarchical Neural Embeddings

Automatic synthesis of realistic co-speech gestures is an increasingly important yet challenging task in artificial embodied agent creation. Previous systems mainly focus on generating gestures in an end-to-end manner, which leads to difficulties in mining the clear rhythm and semantics due to the complex yet subtle harmony between speech and gestures. We present a novel co-speech gesture synthesis method that achieves convincing results both on the rhythm and semantics. For the rhythm, our system contains a robust rhythm-based segmentation pipeline to ensure the temporal coherence between the vocalization and gestures explicitly. For the gesture semantics, we devise a mechanism to effectively disentangle both low- and high-level neural embeddings of speech and motion based on linguistic theory. The high-level embedding corresponds to semantics, while the low-level embedding relates to subtle variations. Lastly, we build correspondence between the hierarchical embeddings of the speech and the motion, resulting in rhythm- and semantics-aware gesture synthesis. Evaluations with existing objective metrics, a newly proposed rhythmic metric, and human feedback show that our method outperforms state-of-the-art systems by a clear margin.