HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

Efficient multi-bounce lightmap creation using GPU forward mapping

Computer graphics can nowadays produce images in realtime that are hard to distinguish from photos of a real scene. One of the most important aspects to achieve this is the interaction of light with materials in the virtual scene. The lighting computation can be separated in two different parts. The first part is concerned with the direct illumination that is applied to all surfaces lit by a light source; algorithms related to this have been greatly improved over the last decades and together with the improvements of the graphics hardware can now produce realistic effects. The second aspect is about the indirect illumination which describes the multiple reflections of light from each surface. In reality, light that hits a surface is never fully absorbed, but instead reflected back into the scene. And even this reflected light is then reflected again and again until its energy is depleted. These multiple reflections make indirect illumination very computationally expensive. The first problem regarding indirect illumination is therefore, how it can be simplified to compute it faster. Another question concerning indirect illumination is, where to compute it. It can either be computed in the fixed image that is created when rendering the scene or it can be stored in a light map. The drawback of the first approach is, that the results need to be recomputed for every frame in which the camera changed. The second approach, on the other hand, is already used for a long time. Once a static scene has been set up, the lighting situation is computed regardless of the time it takes and the result is then stored into a light map. This is a texture atlas for the scene in which each surface point in the virtual scene has exactly one surface point in the 2D texture atlas. When displaying the scene with this approach, the indirect illumination does not need to be recomputed, but is simply sampled from the light map. The main contribution of this thesis is the development of a technique that computes the indirect illumination solution for a scene at interactive rates and stores the result into a light atlas for visualizing it. To achieve this, we overcome two main obstacles. First, we need to be able to quickly project data from any given camera configuration into the parts of the texture that are currently used for visualizing the 3D scene. Since our approach for computing and storing indirect illumination requires a huge amount of these projections, it needs to be as fast as possible. Therefore, we introduce a technique that does this projection entirely on the graphics card with a single draw call. Second, the reflections of light into the scene need to be computed quickly. Therefore, we separate the computation into two steps, one that quickly approximates the spreading of the light into the scene and a second one that computes the visually smooth final result using the aforementioned projection technique. The final technique computes the indirect illumination at interactive rates even for big scenes. It is furthermore very flexible to let the user choose between high quality results or fast computations. This allows the method to be used for quickly editing the lighting situation with high speed previews and then computing the final result in perfect quality at still interactive rates. The technique introduced for projecting data into the texture atlas is in itself highly flexible and also allows for fast painting onto objects and projecting data onto it, considering all perspective distortions and self-occlusions

GAN-Based LiDAR Intensity Simulation

Realistic vehicle sensor simulation is an important element in developing autonomous driving. As physics-based implementations of visual sensors like LiDAR are complex in practice, data-based approaches promise solutions. Using pairs of camera images and LiDAR scans from real test drives, GANs can be trained to translate between them. For this process, we contribute two additions. First, we exploit the camera images, acquiring segmentation data and dense depth maps as additional input for training. Second, we test the performance of the LiDAR simulation by testing how well an object detection network generalizes between real and synthetic point clouds to enable evaluation without ground truth point clouds. Combining both, we simulate LiDAR point clouds and demonstrate their realism

CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees

In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.Comment: Accepted by CVPR 2023 Vision for Agriculture Worksho

Interactive Sampling and Rendering for Complex and Procedural Geometry

International audienceWe present a new sampling method for procedural and complex geometries, which allows interactive point-based modeling and rendering of such scenes. For a variety of scenes, object-space point sets can be generated rapidly, resulting in a sufficiently dense sampling of the final image. We present an integrated approach that exploits the simplicity of the point primitive. For procedural objects a hierarchical sampling scheme is presented that adapts sample densities locally according to the projected size in the image. Dynamic procedural objects and interactive user manipulation thus become possible. The same scheme is also applied to on-the-fly generation and rendering of terrains, and enables the use of an efficient occlusion culling algorithm. Furthermore, by using points the system enables interactive rendering and simple modification of complex objects (e.g., trees). For display, hardware-accelerated 3-D point rendering is used, but our sampling method can be used by any other point-rendering approach

Isotropic clustering for hierarchical radiosity - implementation and experiences

Although Hierarchical Radiosity was a big step forward for finite element computations in the context of global illumination, the algorithm can hardly cope with scenes of more than medium complexity. The reason is that Hierarchical Radiosity requires an initial linking step, comparing all pairs of initial objects in the scene. These initial objects are then hierarchically subdivided in order to accurately represent the light transport between them. Isotropic Clustering, as introduced by Sillion, in addition creates a hierarchy above the input objects. Thus, it allows for the interaction of complete clusters of objects and avoids the costly initial linking step. In this paper, we describe our implementation of the isotropic clustering algorithm and discuss some of the problems that we encountered. The complexity of the algorithm is examined and clustering strategies are compared

Minutes, Arts & Sciences Student Life Committee Meeting, Thursday, February 11, 2010

Major white matter tracts are bundles of neuronal fibers connecting the cortical brain areas to deep seated regions and periphery. An example is the pyramidal tract, which is responsible for motor function, or the corpus callosum connecting both brain hemispheres. Their preservation during brain surgery is of major importance, in order to avoid postoperative new neurological deficits, such as impairment of motor function

LiveNVS: Neural View Synthesis on Live RGB-D Streams

Existing real-time RGB-D reconstruction approaches, like Kinect Fusion, lack real-time photo-realistic visualization. This is due to noisy, oversmoothed or incomplete geometry and blurry textures which are fused from imperfect depth maps and camera poses. Recent neural rendering methods can overcome many of such artifacts but are mostly optimized for offline usage, hindering the integration into a live reconstruction pipeline. In this paper, we present LiveNVS, a system that allows for neural novel view synthesis on a live RGB-D input stream with very low latency and real-time rendering. Based on the RGB-D input stream, novel views are rendered by projecting neural features into the target view via a densely fused depth map and aggregating the features in image-space to a target feature map. A generalizable neural network then translates the target feature map into a high-quality RGB image. LiveNVS achieves state-of-the-art neural rendering quality of unknown scenes during capturing, allowing users to virtually explore the scene and assess reconstruction quality in real-time.Comment: main paper: 8 pages, total number of pages: 15, 13 figures, to be published in SIGGRAPH Asia 2023 Conference Papers; edits: link was fixe