145 research outputs found
Fab forms: customizable objects for fabrication with validity and geometry caching
We address the problem of allowing casual users to customize parametric models while maintaining their valid state as 3D-printable functional objects. We define Fab Form as any design representation that lends itself to interactive customization by a novice user, while remaining valid and manufacturable. We propose a method to achieve these Fab Form requirements for general parametric designs tagged with a general set of automated validity tests and a small number of parameters exposed to the casual user. Our solution separates Fab Form evaluation into a precomputation stage and a runtime stage. Parts of the geometry and design validity (such as manufacturability) are evaluated and stored in the precomputation stage by adaptively sampling the design space. At runtime the remainder of the evaluation is performed. This allows interactive navigation in the valid regions of the design space using an automatically generated Web user interface (UI). We evaluate our approach by converting several parametric models into corresponding Fab Forms.National Science Foundation (U.S.) (Grant 1138967
Semantify: Simplifying the Control of 3D Morphable Models using CLIP
We present Semantify: a self-supervised method that utilizes the semantic
power of CLIP language-vision foundation model to simplify the control of 3D
morphable models. Given a parametric model, training data is created by
randomly sampling the model's parameters, creating various shapes and rendering
them. The similarity between the output images and a set of word descriptors is
calculated in CLIP's latent space. Our key idea is first to choose a small set
of semantically meaningful and disentangled descriptors that characterize the
3DMM, and then learn a non-linear mapping from scores across this set to the
parametric coefficients of the given 3DMM. The non-linear mapping is defined by
training a neural network without a human-in-the-loop. We present results on
numerous 3DMMs: body shape models, face shape and expression models, as well as
animal shapes. We demonstrate how our method defines a simple slider interface
for intuitive modeling, and show how the mapping can be used to instantly fit a
3D parametric body shape to in-the-wild images
Neural Font Rendering
Recent advances in deep learning techniques and applications have
revolutionized artistic creation and manipulation in many domains (text,
images, music); however, fonts have not yet been integrated with deep learning
architectures in a manner that supports their multi-scale nature. In this work
we aim to bridge this gap, proposing a network architecture capable of
rasterizing glyphs in multiple sizes, potentially paving the way for easy and
accessible creation and manipulation of fonts
Boxelization: folding 3D objects into boxes
We present a method for transforming a 3D object into a cube or a box using a continuous folding sequence. Our method produces a single, connected object that can be physically fabricated and folded from one shape to the other. We segment the object into voxels and search for a voxel-tree that can fold from the input shape to the target shape. This involves three major steps: finding a good voxelization, finding the tree structure that can form the input and target shapes' configurations, and finding a non-intersecting folding sequence. We demonstrate our results on several input 3D objects and also physically fabricate some using a 3D printer
PersonalTailor: Personalizing 2D Pattern Design from 3D Garment Point Clouds
Garment pattern design aims to convert a 3D garment to the corresponding 2D
panels and their sewing structure. Existing methods rely either on template
fitting with heuristics and prior assumptions, or on model learning with
complicated shape parameterization. Importantly, both approaches do not allow
for personalization of the output garment, which today has increasing demands.
To fill this demand, we introduce PersonalTailor: a personalized 2D pattern
design method, where the user can input specific constraints or demands (in
language or sketch) for personal 2D panel fabrication from 3D point clouds.
PersonalTailor first learns a multi-modal panel embeddings based on
unsupervised cross-modal association and attentive fusion. It then predicts a
binary panel masks individually using a transformer encoder-decoder framework.
Extensive experiments show that our PersonalTailor excels on both personalized
and standard pattern fabrication tasks.Comment: Technical Repor
Ordered Attention for Coherent Visual Storytelling
We address the problem of visual storytelling, i.e., generating a story for a
given sequence of images. While each sentence of the story should describe a
corresponding image, a coherent story also needs to be consistent and relate to
both future and past images. To achieve this we develop ordered image attention
(OIA). OIA models interactions between the sentence-corresponding image and
important regions in other images of the sequence. To highlight the important
objects, a message-passing-like algorithm collects representations of those
objects in an order-aware manner. To generate the story's sentences, we then
highlight important image attention vectors with an Image-Sentence Attention
(ISA). Further, to alleviate common linguistic mistakes like repetitiveness, we
introduce an adaptive prior. The obtained results improve the METEOR score on
the VIST dataset by 1%. In addition, an extensive human study verifies
coherency improvements and shows that OIA and ISA generated stories are more
focused, shareable, and image-grounded.Comment: 9 pages, 7 figure
Cone carving for surface reconstruction
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