14,925 research outputs found
Geometry-Aware Face Completion and Editing
Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked region. Firstly, a facial geometry estimator is learned to
estimate facial landmark heatmaps and parsing maps from the unmasked face
image. Then, an encoder-decoder structure generator serves to complete a face
image and disentangle its mask areas conditioned on both the masked face image
and the estimated facial geometry images. Besides, since low-rank property
exists in manually labeled masks, a low-rank regularization term is imposed on
the disentangled masks, enforcing our completion network to manage occlusion
area with various shape and size. Furthermore, our network can generate diverse
results from the same masked input by modifying estimated facial geometry,
which provides a flexible mean to edit the completed face appearance. Extensive
experimental results qualitatively and quantitatively demonstrate that our
network is able to generate visually pleasing face completion results and edit
face attributes as well
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
ControlDreamer: Stylized 3D Generation with Multi-View ControlNet
Recent advancements in text-to-3D generation have significantly contributed
to the automation and democratization of 3D content creation. Building upon
these developments, we aim to address the limitations of current methods in
generating 3D models with creative geometry and styles. We introduce multi-view
ControlNet, a novel depth-aware multi-view diffusion model trained on generated
datasets from a carefully curated text corpus. Our multi-view ControlNet is
then integrated into our two-stage pipeline, ControlDreamer, enabling
text-guided generation of stylized 3D models. Additionally, we present a
comprehensive benchmark for 3D style editing, encompassing a broad range of
subjects, including objects, animals, and characters, to further facilitate
research on diverse 3D generation. Our comparative analysis reveals that this
new pipeline outperforms existing text-to-3D methods as evidenced by human
evaluations and CLIP score metrics.Comment: Project page: https://controldreamer.github.io
- …