Efficient 3D Full-Body Motion Generation from Sparse Tracking Inputs with Temporal Windows

TL;DR

This paper introduces an MLP-based approach for 3D full-body motion generation from sparse inputs, effectively balancing accuracy and computational efficiency for resource-limited AR/VR applications.

Contribution

The paper proposes a novel NN-mechanism that divides input sequences into smaller windows and merges information through latent representations, improving performance and efficiency.

Findings

Significantly improved generation accuracy over state-of-the-art methods

Reduced computational costs and memory overhead

Suitable for resource-constrained devices

Abstract

To have a seamless user experience on immersive AR/VR applications, the importance of efficient and effective Neural Network (NN) models is undeniable, since missing body parts that cannot be captured by limited sensors should be generated using these models for a complete 3D full-body reconstruction in virtual environment. However, the state-of-the-art NN-models are typically computational expensive and they leverage longer sequences of sparse tracking inputs to generate full-body movements by capturing temporal context. Inevitably, longer sequences increase the computation overhead and introduce noise in longer temporal dependencies that adversely affect the generation performance. In this paper, we propose a novel Multi-Layer Perceptron (MLP)-based method that enhances the overall performance while balancing the computational cost and memory overhead for efficient 3D full-body generation. Precisely, we introduce a NN-mechanism that divides the longer sequence of inputs into smaller temporal windows. Later, the current motion is merged with the information from these windows through latent representations to utilize the past context for the generation. Our experiments demonstrate that generation accuracy of our method with this NN-mechanism is significantly improved compared to the state-of-the-art methods while greatly reducing computational costs and memory overhead, making our method suitable for resource-constrained devices.