Personalized Dialogue Generation (PDG) aims to create coherent responses
according to roles or personas. Traditional PDG relies on external role data,
which can be scarce and raise privacy concerns. Approaches address these issues
by extracting role information from dialogue history, which often fail to
generically model roles in continuous space. To overcome these limitations, we
introduce a novel framework \textbf{MO}dels \textbf{R}oles from
\textbf{P}ersonalized Dialogue \textbf{H}istory by \textbf{E}xploring and
\textbf{U}tilizing Latent \textbf{S}pace (MORPHEUS) through a three-stage
training process. Specifically, we create a persona codebook to represent roles
in latent space compactly, and this codebook is used to construct a posterior
distribution of role information. This method enables the model to generalize
across roles, allowing the generation of personalized dialogues even for unseen
roles. Experiments on both Chinese and English datasets demonstrate that
MORPHEUS enhances the extraction of role information, and improves response
generation without external role data. Additionally, MORPHEUS can be considered
an efficient fine-tuning for large language models.

通过在对话历史中提取角色信息并构建潜在空间中的角色代码书，MORPHEUS 框架通过三阶段训练过程使模型能够泛化到不同角色，从而实现个性化对话生成，无需外部角色数据，并且可以作为大型语言模型的高效微调。

MORPHEUS：从个性化对话历史中探索和利用潜在空间建模角色

MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring  and Utilizing Latent Space

Neural rendering has demonstrated remarkable success in dynamic scene
reconstruction. Thanks to the expressiveness of neural representations, prior
works can accurately capture the motion and achieve high-fidelity
reconstruction of the target object. Despite this, real-world video scenarios
often feature large unobserved regions where neural representations struggle to
achieve realistic completion. To tackle this challenge, we introduce MorpheuS,
a framework for dynamic 360{\deg} surface reconstruction from a casually
captured RGB-D video. Our approach models the target scene as a canonical field
that encodes its geometry and appearance, in conjunction with a deformation
field that warps points from the current frame to the canonical space. We
leverage a view-dependent diffusion prior and distill knowledge from it to
achieve realistic completion of unobserved regions. Experimental results on
various real-world and synthetic datasets show that our method can achieve
high-fidelity 360{\deg} surface reconstruction of a deformable object from a
monocular RGB-D video.

实现从单目 RGB-D 视频中对可变形物体进行高保真度的 360° 表面重建的 Neural rendering 技术的研究。

MorpheuS: 从单目 RGB-D 视频中的神经动态 360° 表面重建

MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular  RGB-D Video

We present Morpheus, a new model for generating pixel-level morphological
classifications of astronomical sources. Morpheus leverages advances in deep
learning to perform source detection, source segmentation, and morphological
classification pixel-by-pixel via a semantic segmentation algorithm adopted
from the field of computer vision. By utilizing morphological information about
the flux of real astronomical sources during object detection, Morpheus shows
resiliency to false-positive identifications of sources. We evaluate Morpheus
by performing source detection, source segmentation, morphological
classification on the Hubble Space Telescope data in the five CANDELS fields
with a focus on the GOODS South field, and demonstrate a high completeness in
recovering known GOODS South 3D-HST sources with H < 26 AB. We release the code
publicly, provide online demonstrations, and present an interactive
visualization of the Morpheus results in GOODS South.

利用深度学习和语义分割算法，我们提出了 Morpheus 模型，以逐像素级别生成天文源的形态分类。我们在 Hubble Space Telescope 数据上评估了 Morpheus，展示了在 GOODS South 领域中，对 H < 26 AB 的已知源的高度完整性，并提供了在线演示和交互式可视化结果。