ROAM: a Rich Object Appearance Model with Application to Rotoscoping

Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline of this object. This model combines a collection of local foreground/background appearance models spread along the outline, a global appearance model of the enclosed object and a set of distinctive foreground landmarks. The structure of this rich appearance model allows simple initialization, efficient iterative optimization with exact minimization at each step, and on-line adaptation in videos. We demonstrate qualitatively and quantitatively the merit of this framework through comparisons with tools based on either dynamic segmentation with a closed curve or pixel-wise binary labelling

Guided Time Warping for Motion Editing

Time warping allows users to modify timing without affecting poses. It has many applications in animation systems for motion editing, such as refining motions to meet new timing constraints or modifying the acting of animated characters. However, time warping typically requires many manual adjustments to achieve the desired results. We present a technique which simplifies this process by allowing time warps to be guided by a provided reference motion. Given few timing constraints, it computes a warp that both satisfies these constraints and maximizes local timing similarities to the reference. The algorithm is fast enough to incorporate into standard animation workflows. We apply the technique to two common tasks: preserving the natural timing of motions under new time constraints and modifying the timing of motions for stylistic effects.Singapore-MIT GAMBIT Game La

Automatic non-linear video editing for home video collections

The video editing process consists of deciding what elements to retain, delete, or combine from various video sources so that they come together in an organized, logical, and visually pleasing manner. Before the digital era, non-linear editing involved the arduous process of physically cutting and splicing video tapes, and was restricted to the movie industry and a few video enthusiasts. Today, when digital cameras and camcorders have made large personal video collections commonplace, non-linear video editing has gained renewed importance and relevance. Almost all available video editing systems today are dependent on considerable user interaction to produce coherent edited videos. In this work, we describe an automatic non-linear video editing system for generating coherent movies from a collection of unedited personal videos. Our thesis is that computing image-level visual similarity in an appropriate manner forms a good basis for automatic non-linear video editing. To our knowledge, this is a novel approach to solving this problem. The generation of output video from the system is guided by one or more input keyframes from the user, which guide the content of the output video. The output video is generated in a manner such that it is non-repetitive and follows the dynamics of the input videos. When no input keyframes are provided, our system generates "video textures" with the content of the output chosen at random. Our system demonstrates promising results on large video collections and is a first step towards increased automation in non-linear video editin

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Interactive videos: Plausible video editing using sparse structure points

Video remains the method of choice for capturing temporal events. However, without access to the underlying 3D scene models, it remains difficult to make object level edits in a single video or across multiple videos. While it may be possible to explicitly reconstruct the 3D geometries to facilitate these edits, such a workflow is cumbersome, expensive, and tedious. In this work, we present a much simpler workflow to create plausible editing and mixing of raw video footage using only sparse structure points (SSP) directly recovered from the raw sequences. First, we utilize user-scribbles to structure the point representations obtained using structure-from-motion on the input videos. The resultant structure points, even when noisy and sparse, are then used to enable various video edits in 3D, including view perturbation, keyframe animation, object duplication and transfer across videos, etc. Specifically, we describe how to synthesize object images from new views adopting a novel image-based rendering technique using the SSPs as proxy for the missing 3D scene information. We propose a structure-preserving image warping on multiple input frames adaptively selected from object video, followed by a spatio-temporally coherent image stitching to compose the final object image. Simple planar shadows and depth maps are synthesized for objects to generate plausible video sequence mimicking real-world interactions. We demonstrate our system on a variety of input videos to produce complex edits, which are otherwise difficult to achieve