10411 Abstracts Collection -- Computational Video

From 10.10.2010 to 15.10.2010, the Dagstuhl Seminar 10411 ``Computational Video \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

SculptFlow: Visualizing Sculpting Sequences by Continuous Summarization

Digital sculpting is becoming ubiquitous for modeling organic shapes like characters. Artists commonly show their sculpting sessions by producing timelapses or speedup videos. But the long length of these sessions make these visualizations either too long to remain interesting or too fast to be useful. In this paper, we present SculptFlow, an algorithm that summarizes sculpted mesh sequences by repeatedly merging pairs of subsequent edits taking into account the number of summarized strokes, the magnitude of the edits, and whether they overlap. Summaries of any length are generated by stopping the merging process when the desired length is reached. We enhance the summaries by highlighting edited regions and drawing filtered strokes to indicate artists\u27 workflows. We tested SculptFlow by recording professional artists as they modeled a variety of meshes, from detailed heads to full bodies. When compared to speedup videos, we believe that SculptFlow produces more succinct and informative visualizations. We open source SculptFlow for artists to show their work and release all our datasets so that others can improve upon our work

3DFlow: Continuous Summarization of Mesh Editing Workflows

Mesh editing software is continually improving allowing more detailed meshes to be create efficiently by skilled artists. Many of these are interested in sharing not only the final mesh, but also their whole workflows both for creating tutorials as well as for showcasing the artist\u27s talent, style, and expertise. Unfortunately, while creating meshes is improving quickly, sharing editing workflows remains cumbersome since time-lapsed or sped-up videos remain the most common medium. In this paper, we present 3DFlow, an algorithm that computes continuous summarizations of mesh editing workflows. 3DFlow takes as input a sequence of meshes and outputs a visualization of the workflow summarized at any level of detail. The output is enhanced by highlighting edited regions and, if provided, overlaying visual annotations to indicated the artist\u27s work, e.g. summarizing brush strokes in sculpting. We tested 3DFlow with a large set of inputs using a variety of mesh editing techniques, from digital sculpting to low-poly modeling, and found 3DFlow performed well for all. Furthermore, 3DFlow is independent of the modeling software used since it requires only mesh snapshots, using additional information only for optional overlays. We open source 3DFlow for artists to showcase their work and release all our datasets so other researchers can improve upon our work

LOCALIZED TEMPORAL PROFILE OF SURVEILLANCE VIDEO

Surveillance videos are recorded pervasively and their retrieval currently still relies on human operators. As an intermediate representation, this work develops a new temporal profile of video to convey accurate temporal information in the video while keeping certain spatial characteristics of targets of interest for recognition. The profile is obtained at critical positions where major target flow appears. We set a sampling line crossing the motion direction to profile passing targets in the temporal domain. In order to add spatial information to the temporal profile to certain extent, we integrate multiple profiles from a set of lines with blending method to reflect the target motion direction and position in the temporal profile. Different from mosaicing/montage methods for video synopsis in spatial domain, our temporal profile has no limit on the time length, and the created profile significantly reduces the data size for brief indexing and fast search of video

Blind Video Deflickering by Neural Filtering with a Flawed Atlas

Many videos contain flickering artifacts. Common causes of flicker include video processing algorithms, video generation algorithms, and capturing videos under specific situations. Prior work usually requires specific guidance such as the flickering frequency, manual annotations, or extra consistent videos to remove the flicker. In this work, we propose a general flicker removal framework that only receives a single flickering video as input without additional guidance. Since it is blind to a specific flickering type or guidance, we name this "blind deflickering." The core of our approach is utilizing the neural atlas in cooperation with a neural filtering strategy. The neural atlas is a unified representation for all frames in a video that provides temporal consistency guidance but is flawed in many cases. To this end, a neural network is trained to mimic a filter to learn the consistent features (e.g., color, brightness) and avoid introducing the artifacts in the atlas. To validate our method, we construct a dataset that contains diverse real-world flickering videos. Extensive experiments show that our method achieves satisfying deflickering performance and even outperforms baselines that use extra guidance on a public benchmark.Comment: To appear in CVPR2023. Code: github.com/ChenyangLEI/All-In-One-Deflicker Website: chenyanglei.github.io/deflicke

Scented Sliders for Procedural Textures

International audienceProcedural textures often expose a set of parameters controlling their final appearance. This lets end users tune the final look and feel, typically through a set of sliders. However, it is difficult to predict the changes introduced by a given slider, especially as sliders interact in non-trivial ways. We augment the sliders controlling parameters with visual previews revealing the changes that will be introduced upon manipulation. These previews are constantly refreshed to reflect changes with respect to the current settings. The main challenge is to generate the visual sliders in a very limited pixel space and at an interactive rate. This is done by synthesizing the visual slider from a small set of patches ordered in accordance with the slider. These patches are chosen so as to reveal as much as possible the visual variations induced by the slider. The selection and ordering are achieved by using the seam-carving algorithm to carve patches with low visual impact. The obtained patches are then stitched together using patch-based texture synthesis to form the final visual slider

Temporal Mapping of Surveillance Video for Indexing and Summarization

This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created