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Marek Dvoroznák

dblp:165/9882 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 4 · 3 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
4 papers
Geometric modeling and processing · 40% Computer animation and physical simulation · 37% Visual content generation and editing · 17%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation › data-driven animation
example-based animation
0.622018
Toonsynth: example-based synthesis of hand-colored cartoon animations · ACM Trans. Graph. 2018
Example-based expressive animation of 2D rigid bodies · ACM Trans. Graph. 2017
Geometric modeling and processing › deformation
as-rigid-as-possible deformation
0.412020
Monster mash: a single-view approach to casual 3D modeling and animation · ACM Trans. Graph. 2020
Geometric modeling and processing
deformation
0.412020
Monster mash: a single-view approach to casual 3D modeling and animation · ACM Trans. Graph. 2020
Geometric modeling and processing › shape modeling
sketch-based modeling
0.412020
Monster mash: a single-view approach to casual 3D modeling and animation · ACM Trans. Graph. 2020
Computer animation and physical simulation › 2d animation
cartoon animation
0.312018
Toonsynth: example-based synthesis of hand-colored cartoon animations · ACM Trans. Graph. 2018
Visual content generation and editing
style transfer
0.312018
Toonsynth: example-based synthesis of hand-colored cartoon animations · ACM Trans. Graph. 2018
Computer animation and physical simulation
rigid body animation
0.312017
Example-based expressive animation of 2D rigid bodies · ACM Trans. Graph. 2017
Visual content generation and editing › image editing
image compositing
0.212015
Decomposing time-lapse paintings into layers · ACM Trans. Graph. 2015
Image and video processing › image decomposition › image separation
layer separation
0.212015
Decomposing time-lapse paintings into layers · ACM Trans. Graph. 2015

Methods — techniques the papers use, named apart from their topics

single-view modeling · 0.4layered deformation model · 0.4style-aware puppet · 0.3skeletal motion retargeting · 0.3parametric deformation synthesis · 0.3non-parametric stylization · 0.3porter-duff over blending · 0.2kubelka-munk layering · 0.2
YearPublicationVenuePosition
2020 Monster mash: a single-view approach to casual 3D modeling and animation
abstract
We present a new framework for sketch-based modeling and animation of 3D organic shapes that can work entirely in an intuitive 2D domain, enabling a playful, casual experience. Unlike previous sketch-based tools, our approach does not require a tedious part-based multi-view workflow with the explicit specification of an animation rig. Instead, we combine 3D inflation with a novel rigidity-preserving, layered deformation model, ARAP-L, to produce a smooth 3D mesh that is immediately ready for animation. Moreover, the resulting model can be animated from a single viewpoint --- and without the need to handle unwanted inter-penetrations, as required by previous approaches. We demonstrate the benefit of our approach on a variety of examples produced by inexperienced users as well as professional animators. For less experienced users, our single-view approach offers a simpler modeling and animating experience than working in a 3D environment, while for professionals, it offers a quick and casual workspace for ideation.
Marek Dvoroznák, Daniel Sýkora, Cassidy J. Curtis, Brian Curless, Olga Sorkine-Hornung, David Salesin
ACM Trans. Graph.1
2018 Toonsynth: example-based synthesis of hand-colored cartoon animations
abstract
We present a new example-based approach for synthesizing hand-colored cartoon animations. Our method produces results that preserve the specific visual appearance and stylized motion of manually authored animations without requiring artists to draw every frame from scratch. In our framework, the artist first stylizes a limited set of known source skeletal animations from which we extract a style-aware puppet that encodes the appearance and motion characteristics of the artwork. Given a new target skeletal motion, our method automatically transfers the style from the source examples to create a hand-colored target animation. Compared to previous work, our technique is the first to preserve both the detailed visual appearance and stylized motion of the original hand-drawn content. Our approach has numerous practical applications including traditional animation production and content creation for games.
Marek Dvoroznák, Wilmot Li, Vladimir G. Kim, Daniel Sýkora
ACM Trans. Graph.1
2017 Example-based expressive animation of 2D rigid bodies
abstract
We present a novel approach to facilitate the creation of stylized 2D rigid body animations. Our approach can handle multiple rigid objects following complex physically-simulated trajectories with collisions, while retaining a unique artistic style directly specified by the user. Starting with an existing target animation (e.g., produced by a physical simulation engine) an artist interactively draws over a sparse set of frames, and the desired appearance and motion stylization is automatically propagated to the rest of the sequence. The stylization process may also be performed in an off-line batch process from a small set of drawn sequences. To achieve these goals, we combine parametric deformation synthesis that generalizes and reuses hand-drawn exemplars, with non-parametric techniques that enhance the hand-drawn appearance of the synthesized sequence. We demonstrate the potential of our method on various complex rigid body animations which are created with an expressive hand-drawn look using notably less manual interventions as compared to traditional techniques.
Marek Dvoroznák, Pierre Bénard, Pascal Barla, Oliver Wang, Daniel Sýkora
ACM Trans. Graph.1
2015 Decomposing time-lapse paintings into layers
abstract
The creation of a painting, in the physical world or digitally, is a process that occurs over time. Later strokes cover earlier strokes, and strokes painted at a similar time are likely to be part of the same object. In the final painting, this temporal history is lost, and a static arrangement of color is all that remains. The rich literature for interacting with image editing history cannot be used. To enable these interactions, we present a set of techniques to decompose a time lapse video of a painting (defined generally to include pencils, markers, etc.) into a sequence of translucent "stroke" images. We present translucency-maximizing solutions for recovering physical (Kubelka and Munk layering) or digital (Porter and Duff "over" blending operation) paint parameters from before/after image pairs. We also present a pipeline for processing real-world videos of paintings capable of handling long-term occlusions, such as the painter's hand and its shadow, color shifts, and noise.
Jianchao Tan, Marek Dvoroznák, Daniel Sýkora, Yotam I. Gingold
ACM Trans. Graph.2