Hair is without doubt one of the most outstanding options of the human physique, impressing with its dynamic qualities that carry scenes to life. Research have constantly demonstrated that dynamic components have a stronger attraction and fascination than static pictures. Social media platforms like TikTok and Instagram witness the each day sharing of huge portrait images as individuals aspire to make their photos each interesting and artistically charming. This drive fuels researchers’ exploration into the realm of animating human hair inside nonetheless pictures, aiming to supply a vivid, aesthetically pleasing, and delightful viewing expertise.
Latest developments within the area have launched strategies to infuse nonetheless pictures with dynamic components, animating fluid substances resembling water, smoke, and fireplace throughout the body. But, these approaches have largely ignored the intricate nature of human hair in real-life images. This text focuses on the creative transformation of human hair inside portrait pictures, which includes translating the image right into a cinemagraph.
A cinemagraph represents an modern brief video format that enjoys favor amongst skilled photographers, advertisers, and artists. It finds utility in varied digital mediums, together with digital ads, social media posts, and touchdown pages. The fascination for cinemagraphs lies of their capacity to merge the strengths of nonetheless pictures and movies. Sure areas inside a cinemagraph characteristic refined, repetitive motions in a brief loop, whereas the rest stays static. This distinction between stationary and transferring components successfully captivates the viewer’s consideration.
By means of the transformation of a portrait picture right into a cinemagraph, full with refined hair motions, the thought is to reinforce the picture’s attract with out detracting from the static content material, making a extra compelling and fascinating visible expertise.
Current methods and business software program have been developed to generate high-fidelity cinemagraphs from enter movies by selectively freezing sure video areas. Sadly, these instruments aren’t appropriate for processing nonetheless pictures. In distinction, there was a rising curiosity in still-image animation. Most of those approaches have centered on animating fluid components resembling clouds, water, and smoke. Nevertheless, the dynamic conduct of hair, composed of fibrous supplies, presents a particular problem in comparison with fluid components. Not like fluid factor animation, which has obtained in depth consideration, the animation of human hair in actual portrait images has been comparatively unexplored.
Animating hair in a static portrait picture is difficult because of the intricate complexity of hair buildings and dynamics. Not like the graceful surfaces of the human physique or face, hair includes a whole bunch of 1000’s of particular person parts, leading to advanced and non-uniform buildings. This complexity results in intricate movement patterns throughout the hair, together with interactions with the top. Whereas there are specialised methods for modeling hair, resembling utilizing dense digital camera arrays and high-speed cameras, they’re usually expensive and time-consuming, limiting their practicality for real-world hair animation.
The paper offered on this article introduces a novel AI methodology for robotically animating hair inside a static portrait picture, eliminating the necessity for person intervention or advanced {hardware} setups. The perception behind this strategy lies within the human visible system’s lowered sensitivity to particular person hair strands and their motions in actual portrait movies, in comparison with artificial strands inside a digitalized human in a digital atmosphere. The proposed resolution is to animate “hair wisps” as a substitute of particular person strands, making a visually pleasing viewing expertise. To realize this, the paper introduces a hair wisp animation module, enabling an environment friendly and automatic resolution. An summary of this framework is illustrated under.
The important thing problem on this context is methods to extract these hair wisps. Whereas associated work, resembling hair modeling, has centered on hair segmentation, these approaches primarily goal the extraction of your complete hair area, which differs from the target. To extract significant hair wisps, the researchers innovatively body hair wisp extraction as an example segmentation downside, the place a person section inside a nonetheless picture corresponds to a hair wisp. By adopting this downside definition, the researchers leverage occasion segmentation networks to facilitate the extraction of hair wisps. This not solely simplifies the hair wisp extraction downside but additionally permits the usage of superior networks for efficient extraction. Moreover, the paper presents the creation of a hair wisp dataset containing actual portrait images to coach the networks, together with a semi-annotation scheme to provide ground-truth annotations for the recognized hair wisps. Some pattern outcomes from the paper are reported within the determine under in contrast with state-of-the-art methods.
This was the abstract of a novel AI framework designed to remodel nonetheless portraits into cinemagraphs by animating hair wisps with pleasing motions with out noticeable artifacts. In case you are and wish to be taught extra about it, please be at liberty to consult with the hyperlinks cited under.
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Daniele Lorenzi obtained his M.Sc. in ICT for Web and Multimedia Engineering in 2021 from the College of Padua, Italy. He’s a Ph.D. candidate on the Institute of Data Know-how (ITEC) on the Alpen-Adria-Universität (AAU) Klagenfurt. He’s at present working within the Christian Doppler Laboratory ATHENA and his analysis pursuits embody adaptive video streaming, immersive media, machine studying, and QoS/QoE analysis.