Gloss-free Sign Language Production (SLP) offers a direct translation of
spoken language sentences into sign language, bypassing the need for gloss
intermediaries. This paper presents the Sign language Vector Quantization
Network, a novel approach to SLP that leverages Vector Quantization to derive
discrete representations from sign pose sequences. Our method, rooted in both
manual and non-manual elements of signing, supports advanced decoding methods
and integrates latent-level alignment for enhanced linguistic coherence.
Through comprehensive evaluations, we demonstrate superior performance of our
method over prior SLP methods and highlight the reliability of Back-Translation
and Fr\'echet Gesture Distance as evaluation metrics.

该论文介绍了一种利用向量量化从手语姿势序列中得出离散表示的手语矢量量化网络的新方法，该方法支持高级解码方法，并整合了潜在级别的对齐以增强语言的一致性，并通过综合评估证明了该方法优于之前的手语生产方法的卓越性能，并突出了反向转译和 Fréchet 手势距离作为评估指标的可靠性

自回归手语制作：基于离散表示的无词汇化方法

Autoregressive Sign Language Production: A Gloss-Free Approach with  Discrete Representations

Animating virtual avatars with free-view control is crucial for various
applications like virtual reality and digital entertainment. Previous studies
have attempted to utilize the representation power of the neural radiance field
(NeRF) to reconstruct the human body from monocular videos. Recent works
propose to graft a deformation network into the NeRF to further model the
dynamics of the human neural field for animating vivid human motions. However,
such pipelines either rely on pose-dependent representations or fall short of
motion coherency due to frame-independent optimization, making it difficult to
generalize to unseen pose sequences realistically. In this paper, we propose a
novel framework MonoHuman, which robustly renders view-consistent and
high-fidelity avatars under arbitrary novel poses. Our key insight is to model
the deformation field with bi-directional constraints and explicitly leverage
the off-the-peg keyframe information to reason the feature correlations for
coherent results. Specifically, we first propose a Shared Bidirectional
Deformation module, which creates a pose-independent generalizable deformation
field by disentangling backward and forward deformation correspondences into
shared skeletal motion weight and separate non-rigid motions. Then, we devise a
Forward Correspondence Search module, which queries the correspondence feature
of keyframes to guide the rendering network. The rendered results are thus
multi-view consistent with high fidelity, even under challenging novel pose
settings. Extensive experiments demonstrate the superiority of our proposed
MonoHuman over state-of-the-art methods.

本论文提出了一种名为 MonoHuman 的新型框架，该框架使用共享双向变形模块和前向对应搜索模块来创造出一个可塑性质高的形变场，其利用离线关键帧信息来推断人体不同部位运动的相关特征，使得生成的虚拟角色在姿势、细节和视角等方面更为真实。与其他现有方法相比，该框架产生的效果更为卓越。

MonoHuman：从单目视频中生成可动的人类神经网络场

MonoHuman: Animatable Human Neural Field from Monocular Video

3D human pose estimation from a single image is a challenging problem,
especially for in-the-wild settings due to the lack of 3D annotated data. We
propose two anatomically inspired loss functions and use them with a
weakly-supervised learning framework to jointly learn from large-scale
in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal
network that exploits temporal and structural cues present in predicted pose
sequences to temporally harmonize the pose estimations. We carefully analyze
the proposed contributions through loss surface visualizations and sensitivity
analysis to facilitate deeper understanding of their working mechanism. Our
complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M
and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics
card.

使用两种解剖启发的损失函数和弱监督学习框架，结合大规模野外 2D 和室内 / 合成 3D 数据共同学习人体姿态估计。还提出了一个简单的时间网络来调和预测的姿态序列，并通过损失表面可视化和敏感性分析仔细分析了所提出的贡献的工作机制。我们的完整流程在 Human3.6M 和 MPI-INF-3DHP 上分别提高了 11.8％和 12％，并在普通图形卡上以 30 FPS 运行。