Generating Realistic Human Motion Sequences for Virtual Scenes with Diverse Contextual Conditioning

Core Concepts

Purposer is a novel learning-based probabilistic model that can generate realistic human motion sequences conditioned on various contextual information, such as scene geometry, past observations, future target poses, and semantic action-object interactions, to populate virtual 3D scenes.

Abstract

The paper presents Purposer, a novel method for generating realistic human motion sequences that can be controlled using diverse contextual information to populate virtual 3D scenes. The key highlights are: Purposer is a learning-based probabilistic model that can exploit different types of conditioning information, including scene geometry, past observations, future target poses, and semantic action-object interactions. The model is built on top of neural discrete representation learning, where human motion is first encoded into a discrete latent space, and then an auto-regressive generative model is trained to predict sequences of latent indices conditioned on the relevant contextual information. A novel conditioning block is designed to handle future conditioning information in the causal auto-regressive model, using a network with two branches to compute separate stacks of features. Purposer can generate realistic motion sequences that interact with the virtual scene, outperforming existing specialized approaches for specific contextual information, both in terms of quality and diversity. The model is trained on short motion sequences but can generate long-term motions by chaining different conditioning configurations, such as "object interaction" and "locomotion", to navigate the scene and interact with objects. Extensive experiments on the HUMANISE and PROX datasets demonstrate the effectiveness of Purposer in generating physically plausible and semantically coherent human motion in diverse virtual scenes.

Stats

"We represent the scene geometry as a point cloud and encode it using PointNet [37]." "We use the HUMANISE dataset, which contains 19.6K human motion sequences in 643 3D scenes, and the PROX dataset, which has 100K frames with pseudo ground truth captured in 12 scenes."

Quotes

"Purposer can generate realistic motion sequences that interact with the virtual scene, outperforming existing specialized approaches for specific contextual information, both in terms of quality and diversity." "The model is trained on short motion sequences but can generate long-term motions by chaining different conditioning configurations, such as 'object interaction' and 'locomotion', to navigate the scene and interact with objects."

Key Insights Distilled From

Purposer: Putting Human Motion Generation in Context

by Nicolas Ugri... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12942.pdf

Deeper Inquiries

How can Purposer's performance be further improved by incorporating additional contextual information, such as audio or language descriptions, to guide the motion generation?

Incorporating additional contextual information, such as audio or language descriptions, can significantly enhance Purposer's performance in guiding motion generation. By integrating audio cues, the model can capture subtle nuances in human motion that may not be evident from visual data alone. For example, audio descriptions of actions or emotions can provide valuable context for generating more realistic and expressive motions. Language descriptions can also offer detailed instructions or constraints for the generated motions, enabling the model to produce more accurate and contextually relevant results. To leverage audio information, Purposer could incorporate audio features extracted from speech or sound signals as additional conditioning inputs. These features could be used to guide the generation of motions that align with the audio context, such as matching the rhythm or intensity of the speech with the motion dynamics. Similarly, language descriptions could be encoded into semantic representations and used to condition the motion generation process, ensuring that the generated motions align with the specified actions or scenarios described in the text. By integrating audio and language descriptions as contextual information, Purposer can achieve a more comprehensive understanding of the desired motions and generate more realistic and contextually relevant human movements in virtual scenes.

What are the potential limitations of the current approach, and how could it be extended to handle more complex human-scene interactions, such as dynamic object manipulation or collaborative tasks?

While Purposer demonstrates impressive capabilities in generating human motion in virtual scenes, there are potential limitations and areas for improvement in handling more complex human-scene interactions. One limitation is the lack of consideration for physics constraints, which may result in unrealistic interpenetrations with scene objects. Incorporating physics-based simulations or constraints could enhance the model's ability to generate physically plausible interactions with objects in the scene. To address more complex interactions like dynamic object manipulation or collaborative tasks, Purposer could be extended with additional modules or components. For dynamic object manipulation, the model could incorporate object dynamics and constraints to simulate realistic interactions between humans and moving objects. This could involve predicting object trajectories, forces exerted during manipulation, and the impact of object movements on human motion. For collaborative tasks, Purposer could be extended to support multi-agent interactions, where multiple human entities interact with each other and the environment. This could involve modeling cooperative actions, communication between agents, and shared goals or tasks. By incorporating collaborative task scenarios, the model can generate more diverse and realistic human behaviors in interactive environments. Overall, extending Purposer to handle more complex human-scene interactions would require integrating physics-based simulations, dynamic object manipulation capabilities, and support for multi-agent collaborative tasks to enhance the realism and versatility of the generated motions.

Given the ability to generate long-term motion sequences by chaining different conditioning configurations, how could Purposer be applied to simulate and analyze human behavior in virtual environments for applications like training autonomous agents or evaluating human-robot interaction scenarios?

Purposer's capability to generate long-term motion sequences by chaining different conditioning configurations opens up various possibilities for simulating and analyzing human behavior in virtual environments for applications like training autonomous agents or evaluating human-robot interaction scenarios. For training autonomous agents, Purposer can be used to generate diverse and realistic human motion sequences that serve as training data for machine learning models. By simulating a wide range of human behaviors in virtual environments, autonomous agents can learn to interpret and respond to human actions more effectively. The generated motion sequences can be used to train reinforcement learning algorithms, imitation learning models, or other AI systems to navigate and interact with humans in realistic scenarios. In evaluating human-robot interaction scenarios, Purposer can simulate complex human behaviors and interactions with robots in virtual environments. By generating motion sequences that reflect different interaction patterns, the model can help researchers analyze how robots should respond to human actions, gestures, or commands. This can be valuable for designing robot behaviors that are intuitive, safe, and socially acceptable in various human-robot interaction settings. By leveraging Purposer's ability to chain different conditioning configurations and generate long-term motion sequences, researchers and developers can create realistic simulations of human behavior in virtual environments, enabling advanced applications in training autonomous agents, evaluating human-robot interactions, and studying human behavior in diverse scenarios.