4,322 research outputs found
Controllable Animation of Fluid Elements in Still Images
We propose a method to interactively control the animation of fluid elements
in still images to generate cinemagraphs. Specifically, we focus on the
animation of fluid elements like water, smoke, fire, which have the properties
of repeating textures and continuous fluid motion. Taking inspiration from
prior works, we represent the motion of such fluid elements in the image in the
form of a constant 2D optical flow map. To this end, we allow the user to
provide any number of arrow directions and their associated speeds along with a
mask of the regions the user wants to animate. The user-provided input arrow
directions, their corresponding speed values, and the mask are then converted
into a dense flow map representing a constant optical flow map (FD). We observe
that FD, obtained using simple exponential operations can closely approximate
the plausible motion of elements in the image. We further refine computed dense
optical flow map FD using a generative-adversarial network (GAN) to obtain a
more realistic flow map. We devise a novel UNet based architecture to
autoregressively generate future frames using the refined optical flow map by
forward-warping the input image features at different resolutions. We conduct
extensive experiments on a publicly available dataset and show that our method
is superior to the baselines in terms of qualitative and quantitative metrics.
In addition, we show the qualitative animations of the objects in directions
that did not exist in the training set and provide a way to synthesize videos
that otherwise would not exist in the real world
Automatic Animation of Hair Blowing in Still Portrait Photos
We propose a novel approach to animate human hair in a still portrait photo.
Existing work has largely studied the animation of fluid elements such as water
and fire. However, hair animation for a real image remains underexplored, which
is a challenging problem, due to the high complexity of hair structure and
dynamics. Considering the complexity of hair structure, we innovatively treat
hair wisp extraction as an instance segmentation problem, where a hair wisp is
referred to as an instance. With advanced instance segmentation networks, our
method extracts meaningful and natural hair wisps. Furthermore, we propose a
wisp-aware animation module that animates hair wisps with pleasing motions
without noticeable artifacts. The extensive experiments show the superiority of
our method. Our method provides the most pleasing and compelling viewing
experience in the qualitative experiments and outperforms state-of-the-art
still-image animation methods by a large margin in the quantitative evaluation.
Project url: \url{https://nevergiveu.github.io/AutomaticHairBlowing/}Comment: Accepted to ICCV 202
DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors
Animating a still image offers an engaging visual experience. Traditional
image animation techniques mainly focus on animating natural scenes with
stochastic dynamics (e.g. clouds and fluid) or domain-specific motions (e.g.
human hair or body motions), and thus limits their applicability to more
general visual content. To overcome this limitation, we explore the synthesis
of dynamic content for open-domain images, converting them into animated
videos. The key idea is to utilize the motion prior of text-to-video diffusion
models by incorporating the image into the generative process as guidance.
Given an image, we first project it into a text-aligned rich context
representation space using a query transformer, which facilitates the video
model to digest the image content in a compatible fashion. However, some visual
details still struggle to be preserved in the resultant videos. To supplement
with more precise image information, we further feed the full image to the
diffusion model by concatenating it with the initial noises. Experimental
results show that our proposed method can produce visually convincing and more
logical & natural motions, as well as higher conformity to the input image.
Comparative evaluation demonstrates the notable superiority of our approach
over existing competitors.Comment: Project page: https://doubiiu.github.io/projects/DynamiCrafte
Animating Unpredictable Effects
Uncanny computer-generated animations of splashing waves, billowing smoke clouds, and characters’ flowing hair have become a ubiquitous presence on screens of all types since the 1980s. This Open Access book charts the history of these digital moving images and the software tools that make them. Unpredictable Visual Effects uncovers an institutional and industrial history that saw media industries conducting more private R&D as Cold War federal funding began to wane in the late 1980s. In this context studios and media software companies took concepts used for studying and managing unpredictable systems like markets, weather, and fluids and turned them into tools for animation. Unpredictable Visual Effects theorizes how these animations are part of a paradigm of control evident across society, while at the same time exploring what they can teach us about the relationship between making and knowing
Scientific Visualization Using the Flow Analysis Software Toolkit (FAST)
Over the past few years the Flow Analysis Software Toolkit (FAST) has matured into a useful tool for visualizing and analyzing scientific data on high-performance graphics workstations. Originally designed for visualizing the results of fluid dynamics research, FAST has demonstrated its flexibility by being used in several other areas of scientific research. These research areas include earth and space sciences, acid rain and ozone modelling, and automotive design, just to name a few. This paper describes the current status of FAST, including the basic concepts, architecture, existing functionality and features, and some of the known applications for which FAST is being used. A few of the applications, by both NASA and non-NASA agencies, are outlined in more detail. Described in the Outlines are the goals of each visualization project, the techniques or 'tricks' used lo produce the desired results, and custom modifications to FAST, if any, done to further enhance the analysis. Some of the future directions for FAST are also described
Recreating Reality: Waltz With Bashir, Persepolis, and the Documentary Genre
This paper examines Ari Folman’s Waltz With Bashir (2008) and Marjane Satrapi’s Persepolis (2007) to elucidate how artists, distributors, and audiences shape and define the porous boundaries of the documentary genre, and how such perceptions are shaped within a digital context. By analyzing how each film represents reality; that is, how documentaries attempt to represent the real world, this paper explores the elements of performativity within animated documentary as a reflection of both the growing fluidity of the documentary genre and the instability of the indexical in a digital age. In a digital context, where the “real” can be manufactured at an increasing rate, stronger skepticism and cynicism push the documentary genre towards more subjective explorations, with animated documentaries serving as a key example of how genre distinctions have fluctuated in response
Fundamental solutions for water wave animation
This paper investigates the use of fundamental solutions for animating detailed linear water surface waves. We first propose an analytical solution for efficiently animating circular ripples in closed form. We then show how to adapt the method of fundamental solutions (MFS) to create ambient waves interacting with complex obstacles. Subsequently, we present a novel wavelet-based discretization which outperforms the state of the art MFS approach for simulating time-varying water surface waves with moving obstacles. Our results feature high-resolution spatial details, interactions with complex boundaries, and large open ocean domains. Our method compares favorably with previous work as well as known analytical solutions. We also present comparisons between our method and real world examples
Tools for 3D scientific visualization in computational aerodynamics
The purpose is to describe the tools and techniques in use at the NASA Ames Research Center for performing visualization of computational aerodynamics, for example visualization of flow fields from computer simulations of fluid dynamics about vehicles such as the Space Shuttle. The hardware used for visualization is a high-performance graphics workstation connected to a super computer with a high speed channel. At present, the workstation is a Silicon Graphics IRIS 3130, the supercomputer is a CRAY2, and the high speed channel is a hyperchannel. The three techniques used for visualization are post-processing, tracking, and steering. Post-processing analysis is done after the simulation. Tracking analysis is done during a simulation but is not interactive, whereas steering analysis involves modifying the simulation interactively during the simulation. Using post-processing methods, a flow simulation is executed on a supercomputer and, after the simulation is complete, the results of the simulation are processed for viewing. The software in use and under development at NASA Ames Research Center for performing these types of tasks in computational aerodynamics is described. Workstation performance issues, benchmarking, and high-performance networks for this purpose are also discussed as well as descriptions of other hardware for digital video and film recording
LivePhoto: Real Image Animation with Text-guided Motion Control
Despite the recent progress in text-to-video generation, existing studies
usually overlook the issue that only spatial contents but not temporal motions
in synthesized videos are under the control of text. Towards such a challenge,
this work presents a practical system, named LivePhoto, which allows users to
animate an image of their interest with text descriptions. We first establish a
strong baseline that helps a well-learned text-to-image generator (i.e., Stable
Diffusion) take an image as a further input. We then equip the improved
generator with a motion module for temporal modeling and propose a carefully
designed training pipeline to better link texts and motions. In particular,
considering the facts that (1) text can only describe motions roughly (e.g.,
regardless of the moving speed) and (2) text may include both content and
motion descriptions, we introduce a motion intensity estimation module as well
as a text re-weighting module to reduce the ambiguity of text-to-motion
mapping. Empirical evidence suggests that our approach is capable of well
decoding motion-related textual instructions into videos, such as actions,
camera movements, or even conjuring new contents from thin air (e.g., pouring
water into an empty glass). Interestingly, thanks to the proposed intensity
learning mechanism, our system offers users an additional control signal (i.e.,
the motion intensity) besides text for video customization.Comment: Project page: https://xavierchen34.github.io/LivePhoto-Page
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