The connection between our 3D surroundings and the descriptive language that
characterizes them would be well-suited for localizing and generating human
motion in context but for one problem. The complexity introduced by multiple
modalities makes capturing this connection challenging with a fixed set of
descriptors. Specifically, closed vocabulary scene encoders, which require
learning text-scene associations from scratch, have been favored in the
literature, often resulting in inaccurate motion grounding. In this paper, we
propose a method that integrates an open vocabulary scene encoder into the
architecture, establishing a robust connection between text and scene. Our
two-step approach starts with pretraining the scene encoder through knowledge
distillation from an existing open vocabulary semantic image segmentation
model, ensuring a shared text-scene feature space. Subsequently, the scene
encoder is fine-tuned for conditional motion generation, incorporating two
novel regularization losses that regress the category and size of the goal
object. Our methodology achieves up to a 30% reduction in the goal object
distance metric compared to the prior state-of-the-art baseline model on the
HUMANISE dataset. This improvement is demonstrated through evaluations
conducted using three implementations of our framework and a perceptual study.
Additionally, our method is designed to seamlessly accommodate future 2D
segmentation methods that provide per-pixel text-aligned features for
distillation.

本文提出了一种方法，将开放词汇场景编码器与架构相结合，建立了文本和场景之间的强大连接；方法通过知识蒸馏从现有的开放词汇语义图像分割模型预训练场景编码器，确保了一个共享的文本 - 场景特征空间，并通过引入两种新的正则化损失，用于回归目标对象的类别和尺寸，在条件运动生成时对场景编码器进行微调；通过在 HUMANISE 数据集上进行的评估和知觉研究，我们的方法相比先前最先进的基准模型，可以使目标对象距离指标减少高达 30%；此外，我们的方法还能无缝适应未来提供每个像素文本对齐特征的 2D 分割方法。