Meta tags:

Headings (most frequently used words):

editing, comparison, with, methods, magic, fixup, streamlining, photo, by, watching, dynamic, videos, abstract, high, level, approach, spatial, recomposition, results, misc, perspective, colorization, fixing, up, photoshop, beyond, real, photos, user, interface, demo, baselines, text, based, reposing, ablations, style, transfer, effect, motion, model, ablation, latent, noise, initialization, limitations, bibtex,

Text of the page (most frequently used words):
the (91), and (37), image (28), edit (23), reference (23), user (20), model (17), from (16), editing (16), that (15), with (14), fixup (13), our (13), magic (12), can (12), for (10), sample (9), objects (9), ours (8), #motion (7), target (7), using (7), coarse (7), videos (6), not (6), noise (6), affine (6), here (6), photorealistic (5), show (5), training (5), flow (5), use (5), edited (5), interface (5), methods (5), directly (5), zoomshop (5), photo (4), preserve (4), identity (4), style (4), starting (4), pure (4), models (4), but (4), this (4), only (4), video (4), warp (4), based (4), global (4), like (4), are (4), comparison (4), prompt (4), text (4), different (4), input (4), sdedit (4), frame (4), source (4), table (3), dynamic (3), new (3), since (3), transfer (3), original (3), than (3), diffusion (3), images (3), during (3), ground (3), truth (3), both (3), frames (3), two (3), where (3), warping (3), segment (3), details (3), transformations (3), how (3), detail (3), runtime (3), fox (3), masa (3), ctrl (3), method (3), edits (3), results (3), time (3), while (3), through (3), clean (3), layout (3), lighting (3), alzayer (2), hadi (2), xia (2), zhihao (2), zhang (2), xuaner (2), cecilia (2), shechtman (2), eli (2), huang (2), jia (2), bin (2), gharbi (2), michael (2), streamlining (2), watching (2), 2025 (2), computing (2), address (2), doi (2), 1145 (2), 3750722 (2), acm (2), spatial (2), bibtex (2), train (2), spatially (2), inserted (2), what (2), above (2), its (2), initialization (2), generate (2), when (2), misalignment (2), between (2), always (2), noisy (2), version (2), level (2), start (2), step (2), align (2), best (2), transform (2), transformation (2), help (2), aligned (2), synthesize (2), more (2), content (2), needed (2), attempt (2), generated (2), see (2), extraction (2), network (2), handle (2), domains (2), shown (2), minutes (2), seconds (2), dragdiffusion (2), dense (2), key (2), 2024 (2), however (2), find (2), reposing (2), duplicate (2), side (2), other (2), instructpix2pix (2), against (2), 2023 (2), less (2), expressive (2), simply (2), ddim (2), inversion (2), reconstruction (2), example (2), faithful (2), users (2), parts (2), yet (2), demo (2), real (2), magicfixup (2), photoshop (2), make (2), fixing (2), get (2), scene (2), perspective (2), regions (2), many (2), they (2), addressing (2), world (2), insight (2), supervise (2), following (2), each (2), then (2), approach (2), fine (2), physical (2), interactions (2), simple (2), contents, template, nerfies, article, alzayer2025magicfixup, author, title, year, publisher, association, machinery, york, usa, issn, 0730, 0301, url, https, org, journal, trans, graph, month, jul, keywords, learning, does, similar, section, will, stylize, rather, preserving, well, limitations, struggle, deep, range, due, inference, sees, variable, never, denoise, denoising, process, very, instead, latent, forward, everything, estimate, per, types, essential, harmonize, mis, better, ablation, happens, pass, completely, unrelated, understand, play, role, influences, some, influence, driven, provided, essentially, passes, appearance, explains, why, has, seen, cartoons, sketches, effect, ablations, motionguidance, augmenting, keep, track, correspondences, dragging, handles, shi, guidance, geng, these, sota, unable, complex, scenarios, golden, cups, cup, drinking, right, reflect, compare, brooks, cao, effort, describe, prompts, want, stress, strictly, note, rely, below, because, reliable, significantly, hand, takes, consistently, faster, run, baselines, created, call, collage, apply, delete, segments, simplified, speed, segmentation, sampling, brevity, filter, out, majority, non, data, notice, still, generalize, providing, beyond, photos, expects, take, advantage, all, tools, provides, puppet, repose, bonsai, lex, finally, have, big, smile, baseline, although, was, trained, any, color, coloring, partial, opacity, brush, much, cleaner, multiple, samples, highlight, diverse, generation, colorization, inspired, liu, 2022, rendering, depths, focal, lengths, required, hours, manual, done, outputs, comparable, create, taken, paper, misc, rearranging, way, quickly, illumination, connecting, pieces, together, moving, focus, recomposition, carry, useful, information, deform, interact, leveraging, datasets, combination, optical, produce, loss, high, propose, generative, given, coarsely, synthesizes, output, follows, prescribed, transfers, adapts, context, defined, powerful, supervision, task, camera, motions, provide, observations, changes, viewpoint, construct, dataset, which, pair, extracted, same, randomly, chosen, intervals, toward, mimic, expected, test, translate, warped, into, pretrained, design, explicitly, enables, closely, specified, segmentations, manipulations, second, order, effects, harmonizing, abstract, enable, cut, paste, those, automatically, code, arxiv, pdf, transactions, graphics, university, maryland, adobe,

Text of the page (random words):
magic fixup magic fixup streamlining photo editing by watching dynamic videos hadi alzayer 1 2 zhihao xia 1 xuaner cecilia zhang 1 eli shechtman 1 jia bin huang 2 michael gharbi 1 1 adobe 2 university of maryland acm transactions on graphics 2025 pdf arxiv demo code bibtex we enable users to edit images with simple a cut and paste like approach and fixup those edits automatically abstract we propose a generative model that given a coarsely edited image synthesizes a photorealistic output that follows the prescribed layout our method transfers fine details from the original image and preserve the identity of its parts yet it adapts it to the lighting and context defined by the new layout our key insight is that videos are a powerful source of supervision for this task objects and camera motions provide many observations of how the world changes with viewpoint lighting and physical interactions we construct an image dataset in which each sample is a pair of source and target frames extracted from the same video at randomly chosen time intervals we warp the source frame toward the target using two motion models that mimic the expected test time user edits we supervise our model to translate the warped image into the ground truth starting from a pretrained diffusion model our model design explicitly enables fine detail transfer from the source frame to the generated image while closely following the user specified layout we show that by using simple segmentations and coarse 2d manipulations we can synthesize a photorealistic edit faithful to the user s input while addressing second order effects like harmonizing the lighting and physical interactions between edited objects high level approach videos carry useful information on how objects deform and interact in the real world leveraging that insight we use video datasets to supervise photo editing as following for each video we sample a reference and target frames then we warp the reference frame using a combination of optical flow warping and affine transformations to be aligned with the target and produce a coarse edit then we train a diffusion based model to clean up the coarse edit by computing the reconstruction loss against the target frame as ground truth spatial recomposition results by spatially rearranging the scene in a coarse way we can quickly clean up the edit and make it photorealistic through magic fixup fixing up the global illumination connecting edited pieces together and addressing moving objects to different regions of focus reference image user edit magic fixup misc editing perspective editing inspired by zoomshop liu et al 2022 we edit the scene perspective by rendering regions at different depths with different focal lengths to clean up the editing zoomshop required as many as 4 hours of manual editing however with magic fixup this can be done in less than 5 seconds the zoomshop outputs are not directly comparable since they do not use the coarse edit we create but they are results taken directly from the zoomshop paper for reference reference image user edit magicfixup ours sdedit zoomshop for reference only colorization although the model was not trained to see any color editing we find that by coloring objects with partial opacity brush we can get a much cleaner edit through magic fixup here we show multiple samples to highlight the diverse generation we get from magic fixup reference image user edit ours sample 1 ours sample 2 baseline sdedit fixing up photoshop since our model only expects the original and edit images as input we can directly do our editing in photoshop and take advantage of all the editing tools it provides here we use puppet warp to repose a bonsai and make lex finally have a big smile reference image user edit magicfixup ours sdedit beyond real photos while we filter out the majority of non photorealistic videos in our training data we notice that magic fixup can still generalize to new domains by providing the reference image to the model through the detail extraction network we can preserve the global image identity reference image user edit magic fixup ours sdedit user interface demo we created a user interface we call it the collage transform where users can simply segment objects or parts and apply affine transformation duplicate or delete the segments as a simplified yet expressive editing interface here we show an example for using our interface we speed up the segmentation and sampling time for brevity comparison with baselines comparison with text based methods here we compare our method against text based editing methods instructpix2pix brooks et al 2023 and masa ctrl cao et al 2023 we attempt our best effort to describe the user edits in text prompts but we want to stress that text is strictly less expressive than simply editing the image directly note that for methods that rely on ddim inversion like masa ctrl below because ddim inversion is not always reliable the reconstruction can be significantly different from the input image as shown in the fox example on the other hand our method takes the edited image directly so it is consistently faithful to the input and faster to run more comparison results here reference image user edit magic fixup ours instructpix2pix masa ctrl prompt reflect the fox to the other side prompt photo of a fox drinking on the right side prompt duplicate the cup on the table prompt two golden cups on table comparison with reposing methods by augmenting our user interface to keep track of dense correspondences we can generate dragging key handles for dragdiffusion shi et al 2024 and the dense flow needed for motion guidance geng et al 2024 however we find that both of these sota methods are unable to handle complex reposing scenarios reference image user edit magic fixup ours dragdiffusion motionguidance runtime 5 seconds runtime 3 minutes runtime 50 minutes ablations style transfer effect what happens if we pass in a reference image that is completely unrelated to the user edit here we attempt to understand how the reference and the edited image play a role in the generated image we see that the reference image influences the global style with some influence on the details but the content is driven by the provided edit so the detail extraction network essentially passes the global appearance of the reference and this explains why the model can handle domains it has not seen during training like cartoons and sketches as shown above reference image user edit sample 1 sample 2 sample 3 motion model ablation during training to align the reference and target frames from a video we use two motion models 1 flow based warping where we use forward warping to warp the reference and 2 coarse affine transformations where we segment everything in the image and estimate the best affine transform per segment to align with the target we show that using both types of transformation is essential as using the flow model can help us preserve the details and using the coarse affine transformations help us harmonize mis aligned objects better and synthesize more content when needed reference image user edit both motion models ours flow motion model only affine motion model only latent noise initialization diffusion models struggle to generate images with deep dynamic range when starting from pure noise due to misalignment between training and inference during training the model always sees a noisy version of the ground truth with variable level of noise but never denoise from pure noise to address this misalignment we start the denoising process from a very noisy version of the user edit instead of step t with pure noise we start from step t 1 reference image user edit starting from our initialization starting from pure noise limitations since we train the model on spatially editing the reference image the model does not preserve the identity of inserted objects similar to what we show in the style transfer section above the model will stylize the inserted objects in the style of the original image rather than preserving its identity well reference image user edit sample 1 sample 2 bibtex article alzayer2025magicfixup author alzayer hadi and xia zhihao and zhang xuaner cecilia and shechtman eli and huang jia bin and gharbi michael title magic fixup streamlining photo editing by watching dynamic videos year 2025 publisher association for computing machinery address new york ny usa issn 0730 0301 url https doi org 10 1145 3750722 doi 10 1145 3750722 journal acm trans graph month jul keywords photorealistic editing spatial editing learning from videos template from nerfies table of contents