Generating Attribute-Aware Human Motions from Textual Prompt
This work addresses a gap in text-driven human motion generation for applications requiring realistic, attribute-specific animations, though it is a pilot exploration and may be incremental in its approach.
The paper tackles the problem of generating human motions from text by incorporating human attributes like age and gender, which previous methods ignored, and introduces a new framework that decouples action semantics from attributes to enable attribute-controlled generation, validated through experiments on a new benchmark dataset.
Text-driven human motion generation has recently attracted considerable attention, allowing models to generate human motions based on textual descriptions. However, current methods neglect the influence of human attributes-such as age, gender, weight, and height-which are key factors shaping human motion patterns. This work represents a pilot exploration for bridging this gap. We conceptualize each motion as comprising both attribute information and action semantics, where textual descriptions align exclusively with action semantics. To achieve this, a new framework inspired by Structural Causal Models is proposed to decouple action semantics from human attributes, enabling text-to-semantics prediction and attribute-controlled generation. The resulting model is capable of generating attribute-aware motion aligned with the user's text and attribute inputs. For evaluation, we introduce a comprehensive dataset containing attribute annotations for text-motion pairs, setting the first benchmark for attribute-aware motion generation. Extensive experiments validate our model's effectiveness.