649 research outputs found
Learning Controllable 3D Diffusion Models from Single-view Images
Diffusion models have recently become the de-facto approach for generative
modeling in the 2D domain. However, extending diffusion models to 3D is
challenging due to the difficulties in acquiring 3D ground truth data for
training. On the other hand, 3D GANs that integrate implicit 3D representations
into GANs have shown remarkable 3D-aware generation when trained only on
single-view image datasets. However, 3D GANs do not provide straightforward
ways to precisely control image synthesis. To address these challenges, We
present Control3Diff, a 3D diffusion model that combines the strengths of
diffusion models and 3D GANs for versatile, controllable 3D-aware image
synthesis for single-view datasets. Control3Diff explicitly models the
underlying latent distribution (optionally conditioned on external inputs),
thus enabling direct control during the diffusion process. Moreover, our
approach is general and applicable to any type of controlling input, allowing
us to train it with the same diffusion objective without any auxiliary
supervision. We validate the efficacy of Control3Diff on standard image
generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various
conditioning inputs such as images, sketches, and text prompts. Please see the
project website (\url{https://jiataogu.me/control3diff}) for video comparisons.Comment: work in progres
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is made to tackle the inverse problem of synthesizing faces from their
respective landmarks. The primary aim of this work is to demonstrate that
information preserved by landmarks (gender in particular) can be further
accentuated by leveraging generative models to synthesize corresponding faces.
Though the problem is particularly challenging due to its ill-posed nature, we
believe that successful synthesis will enable several applications such as
boosting performance of high-level face related tasks using landmark points and
performing dataset augmentation. To this end, a novel face-synthesis method
known as Gender Preserving Generative Adversarial Network (GP-GAN) that is
guided by adversarial loss, perceptual loss and a gender preserving loss is
presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN
that leverages advantages of U-Net and DenseNet architectures. Extensive
experiments and comparison with recent methods are performed to verify the
effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International
Conference on Pattern Recognition (ICPR2018
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
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