Language2Pose: Natural Language Grounded Pose Forecasting (1907.01108v2)

Abstract: Generating animations from natural language sentences finds its applications in a a number of domains such as movie script visualization, virtual human animation and, robot motion planning. These sentences can describe different kinds of actions, speeds and direction of these actions, and possibly a target destination. The core modeling challenge in this language-to-pose application is how to map linguistic concepts to motion animations. In this paper, we address this multimodal problem by introducing a neural architecture called Joint Language to Pose (or JL2P), which learns a joint embedding of language and pose. This joint embedding space is learned end-to-end using a curriculum learning approach which emphasizes shorter and easier sequences first before moving to longer and harder ones. We evaluate our proposed model on a publicly available corpus of 3D pose data and human-annotated sentences. Both objective metrics and human judgment evaluation confirm that our proposed approach is able to generate more accurate animations and are deemed visually more representative by humans than other data driven approaches.

Overview of Language2Pose: Natural Language Grounded Pose Forecasting

The paper presented in "Language2Pose: Natural Language Grounded Pose Forecasting" introduces a novel approach to map natural language descriptions to human motion, which is applicable in areas such as virtual human animation and robot motion planning. This work addresses the challenge of creating a synchronous multimodal space for language and motion by proposing a model named Joint Language-to-Pose (JL2P). The authors focus on training a joint embedding space that captures both linguistic and kinematic features effectively.

Methodology

At the core of their approach, Ahuja and Morency introduce a framework that processes natural language input to generate sequences of 3D human poses. The JL2P model employs a joint embedding space by encoding language through a recurrent neural network (RNN) and poses through a sequence model built with Gated Recurrent Units (GRUs). They leverage curriculum learning, gradually increasing the complexity of training sequences to improve robustness in pose generation. This systematic training begins with simpler, shorter animations before advancing to more complex ones.

The objective function utilizes a Smooth L1 loss, which aids in mitigating issues with outlier data points and ensuring a stable convergence. The distinctiveness of JL2P lies in its integration of curriculum learning and a coupling of its embedding space for simultaneous learning of language and pose representations.

Evaluation

The paper outlines a comprehensive evaluation of JL2P through objective and subjective metrics. The authors utilize Average Positional Error (APE) and Probability of Correct Keypoints (PCK) as objective evaluation measures, comparing JL2P with prior art, such as the model by Lin et al., and various ablated versions of JL2P. Results show a reduction in average positional error by at least 9%, demonstrating the advantages of integrating curriculum learning and joint embedding spaces in model training.

Moreover, a user paper assesses the subjective performance of the model by presenting participants with animation sequences generated from various models. The participants predominantly preferred the animations generated by JL2P over those by other methods, supporting its effective natural language to pose conversion.

Implications and Future Directions

This paper provides a significant contribution to connecting natural language processing with trajectory forecasting by building a framework that successfully translates complex linguistic instructions into dynamic sequences. By showcasing the ability to capture detailed motion concepts such as speed and direction, JL2P offers foundational insights for future research in dynamic scene understanding and interactive AI systems.

The implications of this work extend into various domains requiring high-level control over animations, such as virtual reality, assistive robotics, and autonomous systems. Future work could focus on expanding language representations with richer contextual embeddings and increasing the diversity of action vocabularies. Additionally, integrating more sophisticated neural architectures or leveraging transformers could further enhance the generalization capabilities of pose forecasting models.

The Language2Pose paper exemplifies the innovative blend of AI techniques aiming to bridge the gap between natural language and motion, supporting advancements in multimodal interaction and human-computer interfaces.