Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.Comment: SIGGRAPH 201

Real-Time Chromakey Matting Using Image Statistics

Given a video signal, we generate an alpha matte based on the chromakey information. The computation is done in interactive-time using pixel shaders. To accomplish this, we use Principle Components Analysis to generate a linear transformation matrix where the resulting color triplets Euclidean distance is directly related to the probability that the color exists in the chromakey spectrum. The result of this process is a trimap of the video signals opacity. To solve the alpha matte from the trimap, we minimize an energy function constrained by the trimap with gradient descent. This energy function is based on the least-squared error of overlapping neighborhoods around each pixel and is independent of the background or foreground color

Analyse de l'espace des chemins pour la composition des ombres et lumières

La réalisation des films d'animation 3D s'appuie de nos jours sur les techniques de rendu physiquement réaliste, qui simulent la propagation de la lumière dans chaque scène. Dans ce contexte, les graphistes 3D doivent jouer avec les effets de lumière pour accompagner la mise en scène, dérouler la narration du film, et transmettre son contenu émotionnel aux spectateurs. Cependant, les équations qui modélisent le comportement de la lumière laissent peu de place à l'expression artistique. De plus, l'édition de l'éclairage par essai-erreur est ralentie par les longs temps de rendu associés aux méthodes physiquement réalistes, ce qui rend fastidieux le travail des graphistes. Pour pallier ce problème, les studios d'animation ont souvent recours à la composition, où les graphistes retravaillent l'image en associant plusieurs calques issus du processus de rendu. Ces calques peuvent contenir des informations géométriques sur la scène, ou bien isoler un effet lumineux intéressant. L'avantage de la composition est de permettre une interaction en temps réel, basée sur les méthodes classiques d'édition en espace image. Notre contribution principale est la définition d'un nouveau type de calque pour la composition, le calque d'ombre. Un calque d'ombre contient la quantité d'énergie perdue dans la scène à cause du blocage des rayons lumineux par un objet choisi. Comparée aux outils existants, notre approche présente plusieurs avantages pour l'édition. D'abord, sa signification physique est simple à concevoir : lorsque l'on ajoute le calque d'ombre et l'image originale, toute ombre due à l'objet choisi disparaît. En comparaison, un masque d'ombre classique représente la fraction de rayons bloqués en chaque pixel, une information en valeurs de gris qui ne peut servir que d'approximation pour guider la composition. Ensuite, le calque d'ombre est compatible avec l'éclairage global : il enregistre l'énergie perdue depuis les sources secondaires, réfléchies au moins une fois dans la scène, là où les méthodes actuelles ne considèrent que les sources primaires. Enfin, nous démontrons l'existence d'une surestimation de l'éclairage dans trois logiciels de rendu différents lorsque le graphiste désactive les ombres pour un objet ; notre définition corrige ce défaut. Nous présentons un prototype d'implémentation des calques d'ombres à partir de quelques modifications du Path Tracing, l'algorithme de choix en production. Il exporte l'image originale et un nombre arbitraire de calques d'ombres liés à différents objets en une passe de rendu, requérant un temps supplémentaire de l'ordre de 15% dans des scènes à géométrie complexe et contenant plusieurs milieux participants. Des paramètres optionnels sont aussi proposés au graphiste pour affiner le rendu des calques d'ombres.The production of 3D animated motion picture now relies on physically realistic rendering techniques, that simulate light propagation within each scene. In this context, 3D artists must leverage lighting effects to support staging, deploy the film's narrative, and convey its emotional content to viewers. However, the equations that model the behavior of light leave little room for artistic expression. In addition, editing illumination by trial-and-error is tedious due to the long render times that physically realistic rendering requires. To remedy these problems, most animation studios resort to compositing, where artists rework a frame by associating multiple layers exported during rendering. These layers can contain geometric information on the scene, or isolate a particular lighting effect. The advantage of compositing is that interactions take place in real time, and are based on conventional image space operations. Our main contribution is the definition of a new type of layer for compositing, the shadow layer. A shadow layer contains the amount of energy lost in the scene due to the occlusion of light rays by a given object. Compared to existing tools, our approach presents several advantages for artistic editing. First, its physical meaning is straightforward: when a shadow layer is added to the original image, any shadow created by the chosen object disappears. In comparison, a traditional shadow matte represents the ratio of occluded rays at a pixel, a grayscale information that can only serve as an approximation to guide compositing operations. Second, shadow layers are compatible with global illumination: they pick up energy lost from secondary light sources that are scattered at least once in the scene, whereas the current methods only consider primary sources. Finally, we prove the existence of an overestimation of illumination in three different renderers when an artist disables the shadow of an object; our definition fixes this shortcoming. We present a prototype implementation for shadow layers obtained from a few modifications of path tracing, the main rendering algorithm in production. It exports the original image and any number of shadow layers associated with different objects in a single rendering pass, with an additional 15% time in scenes containing complex geometry and multiple participating media. Optional parameters are also proposed to the artist to fine-tune the rendering of shadow layers

Harmonizer: Learning to Perform White-Box Image and Video Harmonization

Recent works on image harmonization solve the problem as a pixel-wise image translation task via large autoencoders. They have unsatisfactory performances and slow inference speeds when dealing with high-resolution images. In this work, we observe that adjusting the input arguments of basic image filters, e.g., brightness and contrast, is sufficient for humans to produce realistic images from the composite ones. Hence, we frame image harmonization as an image-level regression problem to learn the arguments of the filters that humans use for the task. We present a Harmonizer framework for image harmonization. Unlike prior methods that are based on black-box autoencoders, Harmonizer contains a neural network for filter argument prediction and several white-box filters (based on the predicted arguments) for image harmonization. We also introduce a cascade regressor and a dynamic loss strategy for Harmonizer to learn filter arguments more stably and precisely. Since our network only outputs image-level arguments and the filters we used are efficient, Harmonizer is much lighter and faster than existing methods. Comprehensive experiments demonstrate that Harmonizer surpasses existing methods notably, especially with high-resolution inputs. Finally, we apply Harmonizer to video harmonization, which achieves consistent results across frames and 56 fps at 1080P resolution. Code and models are available at: https://github.com/ZHKKKe/Harmonizer