MotionCLR: Motion Generation and Training-free Editing via Understanding
  Attention Mechanisms
Authors: name: Ling-Hao Chen, name: Wenxun Dai, name: Xuan Ju, name: Shunlin Lu, name: Lei Zhang

This research delves into the problem of interactive editing of human motion
generation. Previous motion diffusion models lack explicit modeling of the
word-level text-motion correspondence and good explainability, hence
restricting their fine-grained editing ability. To address this issue, we
propose an attention-based motion diffusion model, namely MotionCLR, with CLeaR
modeling of attention mechanisms. Technically, MotionCLR models the in-modality
and cross-modality interactions with self-attention and cross-attention,
respectively. More specifically, the self-attention mechanism aims to measure
the sequential similarity between frames and impacts the order of motion
features. By contrast, the cross-attention mechanism works to find the
fine-grained word-sequence correspondence and activate the corresponding
timesteps in the motion sequence. Based on these key properties, we develop a
versatile set of simple yet effective motion editing methods via manipulating
attention maps, such as motion (de-)emphasizing, in-place motion replacement,
and example-based motion generation, etc. For further verification of the
explainability of the attention mechanism, we additionally explore the
potential of action-counting and grounded motion generation ability via
attention maps. Our experimental results show that our method enjoys good
generation and editing ability with good explainability.