Conditional human motion generation is an important topic with many
applications in virtual reality, gaming, and robotics. While prior works have
focused on generating motion guided by text, music, or scenes, these typically
result in isolated motions confined to short durations. Instead, we address the
generation of long, continuous sequences guided by a series of varying textual
descriptions. In this context, we introduce FlowMDM, the first diffusion-based
model that generates seamless Human Motion Compositions (HMC) without any
postprocessing or redundant denoising steps. For this, we introduce the Blended
Positional Encodings, a technique that leverages both absolute and relative
positional encodings in the denoising chain. More specifically, global motion
coherence is recovered at the absolute stage, whereas smooth and realistic
transitions are built at the relative stage. As a result, we achieve
state-of-the-art results in terms of accuracy, realism, and smoothness on the
Babel and HumanML3D datasets. FlowMDM excels when trained with only a single
description per motion sequence thanks to its Pose-Centric Cross-ATtention,
which makes it robust against varying text descriptions at inference time.
Finally, to address the limitations of existing HMC metrics, we propose two new
metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt
transitions.

FlowMDM 是第一个基于扩散的模型，它生成无需后处理或冗余去噪步骤的无缝人体运动组合。通过引入融合位置编码的技术，在绝对阶段恢复了全局运动连贯性，在相对阶段构建了平滑真实的过渡。FlowMDM 能够在 Babel 数据集上取得准确性、真实性和平滑度等方面的最新成果，并且在推理时仅使用每个动作序列的单个描述，通过其面向姿势的跨注意力机制使其对不同文本描述具有鲁棒性。最后，为了解决现有 HMC 度量的局限性，我们提出了两个新的度量指标：峰值加速度和加速度下的面积，以检测突变的转换。

无缝融合位置编码的人体动作组合

Seamless Human Motion Composition with Blended Positional Encodings

In this paper, we propose a language-universal adapter learning framework
based on a pre-trained model for end-to-end multilingual automatic speech
recognition (ASR). For acoustic modeling, the wav2vec 2.0 pre-trained model is
fine-tuned by inserting language-specific and language-universal adapters. An
online knowledge distillation is then used to enable the language-universal
adapters to learn both language-specific and universal features. The linguistic
information confusion is also reduced by leveraging language identifiers
(LIDs). With LIDs we perform a position-wise modification on the multi-head
attention outputs. In the inference procedure, the language-specific adapters
are removed while the language-universal adapters are kept activated. The
proposed method improves the recognition accuracy and addresses the linear
increase of the number of adapters' parameters with the number of languages in
common multilingual ASR systems. Experiments on the BABEL dataset confirm the
effectiveness of the proposed framework. Compared to the conventional
multilingual model, a 3.3% absolute error rate reduction is achieved. The code
is available at: this https URL.

本文提出了一个基于预训练模型的语言通用适配器学习框架，用于端到端的多语言自动语音识别。通过插入特定于语言和语言通用适配器来微调 Wav2Vec 2.0 预训练模型，然后使用在线知识蒸馏来使语言通用适配器学习特定于语言和通用特征，通过利用语言识别器（LID）降低语言信息混淆，进而改善识别准确率，并解决通用多语种 ASR 系统中适配器参数数量随语言数量线性增长的问题，BABEL 数据集上的实验结果验证了所提出框架的有效性，并相比传统的多语种模型取得了 3.3％的绝对误差率的降低。