Manuel Lang

Nonlinear disparity mapping for stereoscopic 3D

This paper addresses the problem of remapping the disparity range of stereoscopic images and video. Such operations are highly important for a variety of issues arising from the production, live broadcast, and consumption of 3D content. Our work is motivated by the observation that the displayed depth and the resulting 3D viewing experience are dictated by a complex combination of perceptual, technological, and artistic constraints. We first discuss the most important perceptual aspects of stereo vision and their implications for stereoscopic content creation. We then formalize these insights into a set of basic disparity mapping operators. These operators enable us to control and retarget the depth of a stereoscopic scene in a nonlinear and locally adaptive fashion. To implement our operators, we propose a new strategy based on stereoscopic warping of the input video streams. From a sparse set of stereo correspondences, our algorithm computes disparity and image-based saliency estimates, and uses them to compute a deformation of the input views so as to meet the target disparities. Our approach represents a practical solution for actual stereo production and display that does not require camera calibration, accurate dense depth maps, occlusion handling, or inpainting. We demonstrate the performance and versatility of our method using examples from live action post-production, 3D display size adaptation, and live broadcast. An additional user study and ground truth comparison further provide evidence for the quality and practical relevance of the presented work.

A system for retargeting of streaming video

We present a novel, integrated system for content-aware video retargeting. A simple and interactive framework combines key frame based constraint editing with numerous automatic algorithms for video analysis. This combination gives content producers high level control of the retargeting process. The central component of our framework is a non-uniform, pixel-accurate warp to the target resolution which considers automatic as well as interactively defined features. Automatic features comprise video saliency, edge preservation at the pixel resolution, and scene cut detection to enforce bilateral temporal coherence. Additional high level constraints can be added by the producer to guarantee a consistent scene composition across arbitrary output formats. For high quality video display we adopted a 2D version of EWA splatting eliminating aliasing artifacts known from previous work. Our method seamlessly integrates into postproduction and computes the reformatting in real-time. This allows us to retarget annotated video streams at a high quality to arbitary aspect ratios while retaining the intended cinematographic scene composition. For evaluation we conducted a user study which revealed a strong viewer preference for our method.

Three-Dimensional Video Postproduction and Processing

This paper gives an overview of the state-of-the-art in 3-D video postproduction and processing as well as an outlook to remaining challenges and opportunities. First, fundamentals of stereography are outlined that set the rules for proper 3-D content creation. Manipulation of the depth composition of a given stereo pair via view synthesis is identified as the key functionality in this context. Basic algorithms are described to adapt and correct fundamental stereo properties such as geometric distortions, color alignment, and stereo geometry. Then, depth image-based rendering is explained as the widely applied solution for view synthesis in 3-D content creation today. Recent improvements of depth estimation already provide very good results. However, in most cases, still interactive workflows dominate. Warping-based methods may become an alternative for some applications in the future, which do not rely on dense and accurate depth estimation. Finally, 2-D to 3-D conversion is covered, which is an important special area for reuse of existing legacy 2-D content in 3-D. Here various advanced algorithms are combined in interactive workflows.

Practical temporal consistency for image-based graphics applications

We present an efficient and simple method for introducing temporal consistency to a large class of optimization driven image-based computer graphics problems. Our method extends recent work in edge-aware filtering, approximating costly global regularization with a fast iterative joint filtering operation. Using this representation, we can achieve tremendous efficiency gains both in terms of memory requirements and running time. This enables us to process entire shots at once, taking advantage of supporting information that exists across far away frames, something that is difficult with existing approaches due to the computational burden of video data. Our method is able to filter along motion paths using an iterative approach that simultaneously uses and estimates per-pixel optical flow vectors. We demonstrate its utility by creating temporally consistent results for a number of applications including optical flow, disparity estimation, colorization, scribble propagation, sparse data up-sampling, and visual saliency computation.

Pose-space animation and transfer of facial details

This paper presents a novel method for real-time animation of highly-detailed facial expressions based on a multi-scale decomposition of facial geometry into large-scale motion and fine-scale details, such as expression wrinkles. Our hybrid animation is tailored to the specific characteristics of large- and fine-scale facial deformations: Large-scale deformations are computed with a fast linear shell model, which is intuitively and accurately controlled through a sparse set of motion-capture markers or user-defined handle points. Fine-scale facial details are incorporated using a novel pose-space deformation technique, which learns the correspondence of sparse measurements of skin strain to wrinkle formation from a small set of example poses. Our hybrid method features real-time animation of highly-detailed faces with realistic wrinkle formation, and allows both large-scale deformations and fine-scale wrinkles to be edited intuitively. Furthermore, our pose-space representation enables the transfer of facial details to novel expressions or other facial models.

Automatic content creation for multiview autostereoscopic displays using image domain warping

Content creation for autostereoscopic display is a widely unresolved task. Typical methods rely on view synthesis based on depth image based rendering. Our method applies purely image domain warping instead. Input video is analyzed and information about sparse disparity, vertical edges and saliency is extracted. A constrained energy minimization problem is formulated and efficiently solved. The resulting image warping functions are used to synthesize novel views. Our approach is fully automatic, accurate, and reliable. Disocclusions and related artifacts are avoided due to smooth, saliency-driven warping functions. Our method also works well for extrapolation of views in a limited range, thus supporting multiview creation from stereo input, which is the most relevant use case scenario

Automatic View Synthesis by Image-Domain-Warping

Today, stereoscopic 3D (S3D) cinema is already mainstream, and almost all new display devices for the home support S3D content. S3D distribution infrastructure to the home is already established partly in the form of 3D Blu-ray discs, video on demand services, or television channels. The necessity to wear glasses is, however, often considered as an obstacle, which hinders broader acceptance of this technology in the home. Multiviewautostereoscopic displays enable a glasses free perception of S3D content for several observers simultaneously, and support head motion parallax in a limited range. To support multiviewautostereoscopic displays in an already established S3D distribution infrastructure, a synthesis of new views from S3D video is needed. In this paper, a view synthesis method based on image-domain-warping (IDW) is presented that automatically synthesizes new views directly from S3D video and functions completely. IDW relies on an automatic and robust estimation of sparse disparities and image saliency information, and enforces target disparities in synthesized images using an image warping framework. Two configurations of the view synthesizer in the scope of a transmission and view synthesis framework are analyzed and evaluated. A transmission and view synthesis system that uses IDW is recently submitted to MPEGs call for proposals on 3D video technology, where it is ranked among the four best performing proposals.

Disparity-Aware Stereo 3D Production Tools

Stereoscopic 3D (S3D) has reached wide levels of adoption in consumer and professional markets. However, production of high quality S3D content is still a difficult and expensive art. Various S3D production tools and systems have been released recently to assist high quality content creation. This paper presents a number of such algorithms, tools and systems developed at Disney Research Zurich, which all make use of disparity-aware processing.

Algorithm and VLSI architecture for real-time 1080p60 video retargeting

Aspect ratio retargeting for streaming video has actively been researched in the past years. While the mobile market with its huge diversity of screen formats is one of the most promising application areas, no existing algorithm is efficient enough to be embedded in such devices. In this work, we devise an efficient video retargeting algorithm by following an algorithm-architecture co-design approach and we present the first FPGA implementation that is able to retarget full HD 1080p video at up to 60 frames per second. We furthermore show that our algorithm can be implemented on embedded processors at interactive framerates. Our hardware architecture only requires a modest amount of hardware resources, and is portable to a dedicated ASIC for the use in consumer electronic devices such as displays or mobile phones.

Depth estimation and depth enhancement by diffusion of depth features

Current trends in video technology indicate a significant increase in spatial and temporal resolution of video data. Recently, a linear-runtime feature diffusion algorithm was presented which aims for fast and accurate processing of such high resolution data. In this paper, we introduce this algorithm from the perspective of image-based depth estimation, expanding upon the algorithm by requiring interview consistency in the depth diffusion process. We also discuss different application scenarios and provide an in-depth analysis of the method in this context.