Intention-Guided Human Motion Generation for Reaching Arbitrary 3D Goals

Core Concepts

WANDR, a data-driven model, generates natural human motions that enable a 3D human avatar to reach arbitrary 3D goals by introducing novel intention features that guide the motion generation process.

Abstract

The paper presents WANDR, a data-driven approach for generating natural human motions that enable a 3D human avatar to reach arbitrary 3D goals. The key contributions are: Intention Features: WANDR introduces novel intention features that guide the motion generation process. These features capture the spatial relationship between the current pose, the goal position, and the time remaining to reach the goal. The intention features include wrist intention, orientation intention, and pelvis intention. Autoregressive Motion Generation: WANDR is designed as a conditional Variational Autoencoder (c-VAE) that generates motion in an autoregressive frame-by-frame fashion. This allows the model to combine motion skills from different datasets and generalize to unseen goal locations. Training on Complementary Datasets: The model is trained on two datasets - AMASS, which provides a wide range of general human motions, and CIRCLE, which focuses on goal-reaching motions. By integrating these datasets, WANDR can learn both navigational skills and precise goal-reaching abilities. Evaluation on Unseen Goals: The model is evaluated on a diverse set of goal locations around the human, including out-of-distribution goals. This comprehensive evaluation setup tests the model's ability to generalize beyond the training data distribution. The results demonstrate that WANDR can generate natural and realistic human motions that successfully reach a wide range of 3D goals, outperforming existing data-driven and reinforcement learning-based approaches.

Stats

The model is trained on a combination of the AMASS and CIRCLE datasets, which provide a diverse set of human motions and goal-reaching behaviors, respectively.

Quotes

"Goals drive our motions. Even the simplest goal can give rise to intricate motions." "Generating this hierarchy of motions, from the overarching goal to the moment-to-moment individual actions, remains a longstanding challenge in computer vision, graphics, and robotics."

Key Insights Distilled From

WANDR: Intention-guided Human Motion Generation

by Markos Dioma... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15383.pdf

Deeper Inquiries

How could the intention features be extended to control multiple body joints simultaneously, beyond just the wrist?

To extend the intention features to control multiple body joints simultaneously, the model could incorporate additional intention vectors for each joint of interest. Each joint could have its own set of intention features that guide the motion towards the goal position. By defining intention features for multiple joints, the model can coordinate the movements of different body parts to reach the goal effectively. This approach would involve creating a comprehensive set of intention vectors that capture the spatial and temporal aspects of each joint's movement towards the goal. By integrating these multi-joint intention features into the model's architecture, it can generate coordinated and natural motions that involve multiple body parts working together to achieve the desired goal.

What other types of goals, beyond static 3D positions, could the model be adapted to handle, such as dynamic or interactive goals?

The model could be adapted to handle a variety of dynamic or interactive goals beyond static 3D positions. Some examples include: Dynamic Goals: Goals that change position or characteristics over time, requiring the model to adapt its motion generation in real-time. This could involve tracking a moving target or reaching a goal that shifts in position during the motion. Interactive Goals: Goals that involve interactions with objects or the environment. The model could be trained to generate motions that not only reach the goal but also manipulate objects, open doors, or perform other interactive tasks. Temporal Goals: Goals that are time-sensitive, where the model needs to reach the goal within a specified timeframe. This could involve generating motions that account for speed variations to meet time constraints. Multi-Step Goals: Goals that require a sequence of actions to achieve, where the model generates a series of motions to reach intermediate goals before reaching the final target. By adapting the model to handle these types of goals, it can enhance its versatility and applicability in various real-world scenarios that involve dynamic and interactive tasks.

How could the model's performance be further improved by incorporating additional datasets or leveraging other machine learning techniques, such as reinforcement learning or diffusion models?

Incorporating Additional Datasets: By integrating more diverse and extensive datasets that cover a wider range of motions and goal-reaching scenarios, the model can improve its generalization and adaptability. Including datasets with varied interactions, object manipulations, and complex motions can enhance the model's ability to generate realistic and goal-oriented human motions. Reinforcement Learning: Combining the data-driven approach with reinforcement learning can help the model learn more complex and expressive motion sequences. Reinforcement learning can provide a principled way to explore the solution space and optimize the model's behavior towards achieving specific goals. By incorporating reinforcement learning techniques, the model can learn to generate more natural and goal-oriented motions efficiently. Diffusion Models: Leveraging diffusion models can enhance the model's capability to generate motions conditioned on textual input or spatial data. Diffusion models have shown success in generating diverse and realistic motions, especially in scenarios where precise goal-reaching is required. By integrating diffusion models into the architecture, the model can improve its motion synthesis quality and generate motions that accurately reach specified target locations or navigate around obstacles. By incorporating additional datasets and leveraging advanced machine learning techniques like reinforcement learning and diffusion models, the model's performance can be further enhanced, leading to more robust and versatile human motion generation capabilities.