Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (1705.06400v2)

Abstract: Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist today require a symbolic representation of motions (e.g. in the form of motion primitives), which have to be defined a-priori or require complex segmentation algorithms. In contrast, recent advances in the field of neural networks and especially deep learning have demonstrated that sub-symbolic representations that can be learned end-to-end usually outperform more traditional approaches, for applications such as machine translation. In this paper we propose a generative model that learns a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (RNNs) and sequence-to-sequence learning. Our approach does not require any segmentation or manual feature engineering and learns a distributed representation, which is shared for all motions and descriptions. We evaluate our approach on 2,846 human whole-body motions and 6,187 natural language descriptions thereof from the KIT Motion-Language Dataset. Our results clearly demonstrate the effectiveness of the proposed model: We show that our model generates a wide variety of realistic motions only from descriptions thereof in form of a single sentence. Conversely, our model is also capable of generating correct and detailed natural language descriptions from human motions.

• The paper introduces an end-to-end generative model using deep RNNs to map between human whole-body motion and natural language.
• It employs separate GRU-based encoder-decoder architectures for both motion-to-language and language-to-motion, evaluated on the KIT Motion-Language Dataset.
• The model’s probabilistic output and semantic embedding techniques enhance natural language descriptions and motion synthesis for human-robot interaction.

Overview of Bidirectional Mapping Between Human Whole-Body Motion and Natural Language

This paper presents a compelling paper on the application of deep recurrent neural networks (RNNs) to develop bidirectional mappings between human whole-body motion and natural language. The research primarily addresses the limitations of traditional symbolic approaches by introducing an end-to-end generative model that leverages sequence-to-sequence learning. Unlike previous methods that depended on a priori segmentation and manual feature engineering of motion data, this model learns distributed representations applicable to a variety of motions and their corresponding descriptions.

Model Architecture and Implementation

The paper introduces two distinct models for the mapping processes: motion-to-language and language-to-motion, both utilizing the sequence-to-sequence architecture. These models include separate encoder and decoder networks built on GRUs, with specific adaptations such as bidirectional processing and layered structural enhancements. Probabilistic outputs are a defining feature, enabling the generation of multiple hypotheses and their subsequent ranking according to likelihood.

For motion data, joint-space representation under the Master Motor Map (MMM) standard is employed, offering a normalized and lower-dimensional structure. Language descriptions are vectored using one-hot encoding with embedding layers subsequently learning these representations. The training processes for both mappings involve standard practices, including dropout regularization and the use of gradient clipping and Nesterov-accelerated Adam optimizers.

Evaluation and Results

The proposed models were evaluated extensively using the KIT Motion-Language Dataset, which offers rich annotations for human whole-body motions. Results showed the models' efficacy in generating plausible and semantically rich natural language descriptions for varied and complex motions. Conversely, they demonstrated the potential to regenerate motion sequences from given natural language descriptions accurately.

The paper employs the Bleu score as a quantitative measure of model performance, offering insight into the trade-off between hypothesis generation ranking and semantic accuracy. Furthermore, qualitative assessments via visualization techniques such as t-distributed Stochastic Neighbor Embedding (t-SNE) indicate that the model captures meaningful semantic relationships within the motion and language embeddings.

Implications and Future Directions

The implications of this research are significant in domains such as human-robot interaction, where understanding and replicating human motion via natural language instructions or vice versa is crucial. This bidirectional mapping can enhance robotic systems' ability to learn from human demonstrations and execute tasks based on verbal commands.

Future work could focus on integrating attention mechanisms to manage longer and more complex sequences effectively. Moreover, expanding datasets to include multi-step activities and enriching the motion data with dynamic information and contact interactions promises to address current limitations such as static pose replication and missing object manipulation context.

In summary, the paper advances the field of multimodal AI by presenting a robust framework for bidirectional transformation between human motion and linguistic descriptions, marking a substantive contribution to the interface of natural language processing and motion synthesis.