54,357 research outputs found
ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis
In this work, we propose a method to address the challenge of rendering a 3D
human from a single image in a free-view manner. Some existing approaches could
achieve this by using generalizable pixel-aligned implicit fields to
reconstruct a textured mesh of a human or by employing a 2D diffusion model as
guidance with the Score Distillation Sampling (SDS) method, to lift the 2D
image into 3D space. However, a generalizable implicit field often results in
an over-smooth texture field, while the SDS method tends to lead to a
texture-inconsistent novel view with the input image. In this paper, we
introduce a texture-consistent back view synthesis module that could transfer
the reference image content to the back view through depth and text-guided
attention injection. Moreover, to alleviate the color distortion that occurs in
the side region, we propose a visibility-aware patch consistency regularization
for texture mapping and refinement combined with the synthesized back view
texture. With the above techniques, we could achieve high-fidelity and
texture-consistent human rendering from a single image. Experiments conducted
on both real and synthetic data demonstrate the effectiveness of our method and
show that our approach outperforms previous baseline methods.Comment: see project page: https://gaoxiangjun.github.io/contex_human
Real-time content-aware texturing for deformable surfaces
Animation of models often introduces distortions to their parameterisation, as these are typically optimised for a single frame. The net effect is that under deformation, the mapped features, i.e. UV texture maps, bump maps or displacement maps, may appear to stretch or scale in an undesirable way. Ideally, what we would like is for the appearance of such features to remain feasible given any underlying deformation. In this paper we introduce a real-time technique that reduces such distortions based on a distortion control (rigidity) map. In two versions of our proposed technique, the parameter space is warped in either an axis or a non-axis aligned manner based on the minimisation of a non-linear distortion metric. This in turn is solved using a highly optimised hybrid CPU-GPU strategy. The result is real-time dynamic content-aware texturing that reduces distortions in a controlled way. The technique can be applied to reduce distortions in a variety of scenarios, including reusing a low geometric complexity animated sequence with a multitude of detail maps, dynamic procedurally defined features mapped on deformable geometry and animation authoring previews on texture-mapped models. © 2013 ACM
TET-GAN: Text Effects Transfer via Stylization and Destylization
Text effects transfer technology automatically makes the text dramatically
more impressive. However, previous style transfer methods either study the
model for general style, which cannot handle the highly-structured text effects
along the glyph, or require manual design of subtle matching criteria for text
effects. In this paper, we focus on the use of the powerful representation
abilities of deep neural features for text effects transfer. For this purpose,
we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a
stylization subnetwork and a destylization subnetwork. The key idea is to train
our network to accomplish both the objective of style transfer and style
removal, so that it can learn to disentangle and recombine the content and
style features of text effects images. To support the training of our network,
we propose a new text effects dataset with as much as 64 professionally
designed styles on 837 characters. We show that the disentangled feature
representations enable us to transfer or remove all these styles on arbitrary
glyphs using one network. Furthermore, the flexible network design empowers
TET-GAN to efficiently extend to a new text style via one-shot learning where
only one example is required. We demonstrate the superiority of the proposed
method in generating high-quality stylized text over the state-of-the-art
methods.Comment: Accepted by AAAI 2019. Code and dataset will be available at
http://www.icst.pku.edu.cn/struct/Projects/TETGAN.htm
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