KMM: Key Frame Mask Mamba for Extended Motion Generation
Authors: name: Zeyu Zhang, name: Hang Gao, name: Akide Liu, name: Qi Chen, name: Feng Chen, name: Yiran Wang, name: Danning Li, name: Hao Tang

Human motion generation is a cut-edge area of research in generative computer
vision, with promising applications in video creation, game development, and
robotic manipulation. The recent Mamba architecture shows promising results in
efficiently modeling long and complex sequences, yet two significant challenges
remain: Firstly, directly applying Mamba to extended motion generation is
ineffective, as the limited capacity of the implicit memory leads to memory
decay. Secondly, Mamba struggles with multimodal fusion compared to
Transformers, and lack alignment with textual queries, often confusing
directions (left or right) or omitting parts of longer text queries. To address
these challenges, our paper presents three key contributions: Firstly, we
introduce KMM, a novel architecture featuring Key frame Masking Modeling,
designed to enhance Mamba's focus on key actions in motion segments. This
approach addresses the memory decay problem and represents a pioneering method
in customizing strategic frame-level masking in SSMs. Additionally, we designed
a contrastive learning paradigm for addressing the multimodal fusion problem in
Mamba and improving the motion-text alignment. Finally, we conducted extensive
experiments on the go-to dataset, BABEL, achieving state-of-the-art performance
with a reduction of more than 57% in FID and 70% parameters compared to
previous state-of-the-art methods. See project website:
https://steve-zeyu-zhang.github.io/KMM