Browsing by Author "Reimann, Max"

Now showing 1 - 5 of 5
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    Approaches for Local Artistic Control of Mobile Neural Style Transfer
    (ACM, 2018) Reimann, Max; Klingbeil, Mandy; Pasewaldt, Sebastian; Semmo, Amir; Döllner, Jürgen; Trapp, Matthias; Aydın, Tunç and Sýkora, Daniel
    This work presents enhancements to state-of-the-art adaptive neural style transfer techniques, thereby providing a generalized user interface with creativity tool support for lower-level local control to facilitate the demanding interactive editing on mobile devices. The approaches are implemented in a mobile app that is designed for orchestration of three neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors to perform location-based filtering and direct the composition. Based on first user tests, we conclude with insights, showing different levels of satisfaction for the implemented techniques and user interaction design, pointing out directions for future research.
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    Art-directable Stroke-based Rendering on Mobile Devices
    (The Eurographics Association, 2023) Wagner, Ronja; Schulz, Sebastian; Reimann, Max; Semmo, Amir; Döllner, Jürgen; Trapp, Matthias; Guthe, Michael; Grosch, Thorsten
    This paper introduces an art-directable stroke-based rendering technique for transforming photos into painterly renditions on mobile devices. Unlike previous approaches that rely on time-consuming iterative computations and explicit brush-stroke geometry, our method offers a interactive image-based implementation tailored to the capabilities of modern mobile devices. The technique places curved brush strokes in multiple passes, leveraging a texture bombing algorithm. To maintain and highlight essential details for stylization, we incorporate additional information such as image salience, depth, and facial landmarks as parameters. Our technique enables a user to control and manipulate using a wide range of parameters and masks during editing to adjust and refine the stylized image. The result is an interactive painterly stylization tool that supports high-resolution input images, providing users with an immersive and engaging artistic experience on their mobile devices.
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    Interactive Control over Temporal Consistency while Stylizing Video Streams
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Shekhar, Sumit; Reimann, Max; Hilscher, Moritz; Semmo, Amir; Döllner, Jürgen; Trapp, Matthias; Ritschel, Tobias; Weidlich, Andrea
    Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per-frame basis. However, per-frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain-agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real-time at full HD resolutions while providing interactive consistency control. We develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state-of-the-art video consistency approaches. maxreimann.github.io/stream-consistency
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    Interactive Photo Editing on Smartphones via Intrinsic Decomposition
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Shekhar, Sumit; Reimann, Max; Mayer, Maximilian; Semmo, Amir; Pasewaldt, Sebastian; Döllner, Jürgen; Trapp, Matthias; Mitra, Niloy and Viola, Ivan
    Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPUbased visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
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    Teaching Data-driven Video Processing via Crowdsourced Data Collection
    (The Eurographics Association, 2021) Reimann, Max; Wegen, Ole; Pasewaldt, Sebastian; Semmo, Amir; Döllner, Jürgen; Trapp, Matthias; Sousa Santos, Beatriz and Domik, Gitta
    This paper presents the concept and experience of teaching an undergraduate course on data-driven image and video processing. When designing visual effects that make use of Machine Learning (ML) models for image-based analysis or processing, the availability of training data typically represents a key limitation when it comes to feasibility and effect quality. The goal of our course is to enable students to implement new kinds of visual effects by acquiring training datasets via crowdsourcing that are used to train ML models as part of a video processing pipeline. First, we propose our course structure and best practices that are involved with crowdsourced data acquisitions. We then discuss the key insights we gathered from an exceptional undergraduate seminar project that tackles the challenging domain of video annotation and learning. In particular, we focus on how to practically develop annotation tools and collect high-quality datasets using Amazon Mechanical Turk (MTurk) in the budget- and time-constrained classroom environment. We observe that implementing the full acquisition and learning pipeline is entirely feasible for a seminar project, imparts hands-on problem solving skills, and promotes undergraduate research.