Human motion generation stands as a significant pursuit in generative
computer vision, while achieving long-sequence and efficient motion generation
remains challenging. Recent advancements in state space models (SSMs), notably
Mamba, have showcased considerable promise in long sequence modeling with an
efficient hardware-aware design, which appears to be a promising direction to
build motion generation model upon it. Nevertheless, adapting SSMs to motion
generation faces hurdles since the lack of a specialized design architecture to
model motion sequence. To address these challenges, we propose Motion Mamba, a
simple and efficient approach that presents the pioneering motion generation
model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba
(HTM) block to process temporal data by ensemble varying numbers of isolated
SSM modules across a symmetric U-Net architecture aimed at preserving motion
consistency between frames. We also design a Bidirectional Spatial Mamba (BSM)
block to bidirectionally process latent poses, to enhance accurate motion
generation within a temporal frame. Our proposed method achieves up to 50% FID
improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets
compared to the previous best diffusion-based method, which demonstrates strong
capabilities of high-quality long sequence motion modeling and real-time human
motion generation. See project website
this https URL

提出了一种简单高效的运动生成模型，利用了状态空间模型进行建模，并通过分层时序 Mamba (HTM) 块和双向空间 Mamba (BSM) 块来处理时序和姿态信息，从而实现了高质量、长序列的运动生成和实时人体动作生成。

Motion Mamba: 高效和长序列动作生成与分层和双向选择性 SSM

Motion Mamba: Efficient and Long Sequence Motion Generation with  Hierarchical and Bidirectional Selective SSM

Transformers do not scale very well to long sequence lengths largely because
of quadratic self-attention complexity. In the recent months, a wide spectrum
of efficient, fast Transformers have been proposed to tackle this problem, more
often than not claiming superior or comparable model quality to vanilla
Transformer models. To this date, there is no well-established consensus on how
to evaluate this class of models. Moreover, inconsistent benchmarking on a wide
spectrum of tasks and datasets makes it difficult to assess relative model
quality amongst many models. This paper proposes a systematic and unified
benchmark, LRA, specifically focused on evaluating model quality under
long-context scenarios. Our benchmark is a suite of tasks consisting of
sequences ranging from $1K$ to $16K$ tokens, encompassing a wide range of data
types and modalities such as text, natural, synthetic images, and mathematical
expressions requiring similarity, structural, and visual-spatial reasoning. We
systematically evaluate ten well-established long-range Transformer models
(Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers,
Synthesizers, Sparse Transformers, and Longformers) on our newly proposed
benchmark suite. LRA paves the way towards better understanding this class of
efficient Transformer models, facilitates more research in this direction, and
presents new challenging tasks to tackle. Our benchmark code will be released
at this https URL

本论文提出了一个系统和统一的基准测试，名为 LRA，专门评估长上下文下的模型质量，其中系统地评估了十个成熟的长范围 Transformer 模型，为更好地理解高效 Transformer 模型铺平了道路，并提出了新的具有挑战性的任务。