MotionScript: Natural Language Descriptions for Expressive 3D Human Motions

This paper proposes MotionScript, a motion-to-text conversion algorithm and natural language representation for human body motions. MotionScript aims to describe movements in greater detail and with more accuracy than previous natural language approaches. Many motion datasets describe relatively objective and simple actions with little variation on the way they are expressed (e.g. sitting, walking, dribbling a ball). But for expressive actions that contain a diversity of movements in the class (e.g. being sad, dancing), or for actions outside the domain of standard motion capture datasets (e.g. stylistic walking, sign-language), more specific and granular natural language descriptions are needed. Our proposed MotionScript descriptions differ from existing natural language representations in that it provides direct descriptions in natural language instead of simple action labels or high-level human captions. To the best of our knowledge, this is the first attempt at translating 3D motions to natural language descriptions without requiring training data. Our experiments show that when MotionScript representations are used in a text-to-motion neural task, body movements are more accurately reconstructed, and large language models can be used to generate unseen complex motions.

Parallel Bayesian Global Optimization of Expensive Functions

We consider parallel global optimization of derivative-free expensive-to-evaluate functions, and propose an efficient method based on stochastic approximation for implementing a conceptual Bayesian optimization algorithm proposed by Ginsbourger et al. (2007). To accomplish this, we use infinitessimal perturbation analysis (IPA) to construct a stochastic gradient estimator and show that this estimator is unbiased. We also show that the stochastic gradient ascent algorithm using the constructed gradient estimator converges to a stationary point of the q-EI surface, and therefore, as the number of multiple starts of the gradient ascent algorithm and the number of steps for each start grow large, the one-step Bayes optimal set of points is recovered. We show in numerical experiments that our method for maximizing the q-EI is faster than methods based on closed-form evaluation using high-dimensional integration, when considering many parallel function evaluations, and is comparable in speed when considering few. We also show that the resulting one-step Bayes optimal algorithm for parallel global optimization finds high quality solutions with fewer evaluations that a heuristic based on approximately maximizing the q-EI. A high quality open source implementation of this algorithm is available in the open source Metrics Optimization Engine (MOE).