KMM: Key Frame Mask Mamba for Extended Motion Generation
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
67331674dfcbdbfec8c1ab64
Zeyu Zhang
claimed_verified
2024-11-15T09:26:26.169Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
67331674dfcbdbfec8c1ab65
Hang Gao
extracted_pending
2024-11-12T08:48:53.847Z
62ba40650e2fb1dcddc6dad1
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Hao Tang
user
Ha0Tang
67331674dfcbdbfec8c1ab66
Akide Liu
67331674dfcbdbfec8c1ab67
Qi Chen
67331674dfcbdbfec8c1ab68
Feng Chen
claimed_verified
2024-12-10T11:16:41.540Z
64574380f3ef144c0e69d484
/avatars/a0a84757cb0bf09c24291803e1389b49.svg
Feng Chen
user
chenfeng1271
67331674dfcbdbfec8c1ab69
Yiran Wang
67331674dfcbdbfec8c1ab6a
Danning Li
67331674dfcbdbfec8c1ab6b
Hao Tang
KMM: Key Frame Mask Mamba for Extended Motion Generation
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
KMM enhances Mamba's motion generation through key frame masking and contrastive learning, achieving state-of-the-art results with reduced parameters.
Key frame Masking Modeling SSMs contrastive learning multimodal fusion memory decay motion-text alignment BABEL FID