EG 2022 - Short Papers

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AvatarGo: Plug and Play self-avatars for VR
(The Eurographics Association, 2022) Ponton, Jose Luis; Monclús, Eva; Pelechano, Nuria; Pelechano, Nuria; Vanderhaeghe, David
The use of self-avatars in a VR application can enhance presence and embodiment which leads to a better user experience. In collaborative VR it also facilitates non-verbal communication. Currently it is possible to track a few body parts with cheap trackers and then apply IK methods to animate a character. However, the correspondence between trackers and avatar joints is typically fixed ad-hoc, which is enough to animate the avatar, but causes noticeable mismatches between the user's body pose and the avatar. In this paper we present a fast and easy to set up system to compute exact offset values, unique for each user, which leads to improvements in avatar movement. Our user study shows that the Sense of Embodiment increased significantly when using exact offsets as opposed to fixed ones. We also allowed the users to see a semitransparent avatar overlaid with their real body to objectively evaluate the quality of the avatar movement with our technique.
EUROGRAPHICS 2022: Short Papers Frontmatter
(The Eurographics Association, 2022) Pelechano, Nuria; Vanderhaeghe, David; Pelechano, Nuria; Vanderhaeghe, David
Fitness of General-Purpose Monocular Depth Estimation Architectures for Transparent Structures
(The Eurographics Association, 2022) Wirth, Tristan; Jamili, Aria; Buelow, Max von; Knauthe, Volker; Guthe, Stefan; Pelechano, Nuria; Vanderhaeghe, David
Due to material properties, monocular depth estimation of transparent structures is inherently challenging. Recent advances leverage additional knowledge that is not available in all contexts, i.e., known shape or depth information from a sensor. General-purpose machine learning models, that do not utilize such additional knowledge, have not yet been explicitly evaluated regarding their performance on transparent structures. In this work, we show that these models show poor performance on the depth estimation of transparent structures. However, fine-tuning on suitable data sets, such as ClearGrasp, increases their estimation performance on the task at hand. Our evaluations show that high performance on general-purpose benchmarks translates well into performance on transparent objects after fine-tuning. Furthermore, our analysis suggests, that state-of-theart high-performing models are not able to capture a high grade of detail from both the image foreground and background at the same time. This finding shows the demand for a combination of existing models to further enhance depth estimation quality.
Graph Partitioning Algorithms for Rigid Body Simulations
(The Eurographics Association, 2022) Liu, Yinchu; Andrews, Sheldon; Pelechano, Nuria; Vanderhaeghe, David
We propose several graph partitioning algorithms for improving the performance of rigid body simulations. The algorithms operate on the graph formed by rigid bodies (nodes) and constraints (edges), producing non-overlapping and contiguous sub-systems that can be simulated in parallel by a domain decomposition technique. We demonstrate that certain partitioning algorithms reduce the computational time of the solver, and graph refinement techniques that reduce coupling between sub-systems, such as the Kernighan-Lin and Fiduccia-Mattheyses algorithms, give additional performance improvements.
Graph-based Computation of Voronoi Diagrams on Large-scale Point-based Surfaces
(The Eurographics Association, 2022) Bletterer, Arnaud; Payan, Frédéric; Antonini, Marc; Pelechano, Nuria; Vanderhaeghe, David
We present an original algorithm to construct Voronoi tessellations on surfaces from a set of depth maps. Based on a local graphbased structure, where each local graph spans one depth map, our algorithm is able to compute partial Voronoi diagrams (one per scan), and then to merge/update them into a single and globally consistent Voronoi diagram. Our first results show that this algorithm is particularly promising for improving the sampling quality of massive point clouds or for reconstructing very large-scale scenes, with low and manageable memory consumption.
A Halfedge Refinement Rule for Parallel Loop Subdivision
(The Eurographics Association, 2022) Vanhoey, Kenneth; Dupuy, Jonathan; Pelechano, Nuria; Vanderhaeghe, David
We observe that a Loop refinement step invariably splits halfedges into four new ones. We leverage this observation to formulate a breadth-first uniform Loop subdivision algorithm: Our algorithm iterates over halfedges to both generate the refined topological information and scatter contributions to the refined vertex points. Thanks to this formulation we limit concurrent data access, enabling straightforward and efficient parallelization on the GPU. We provide an open-source GPU implementation that runs at state-of-the-art performances and supports production-ready assets, including borders and semi-sharp creases.
Improved Lighting Models for Facial Appearance Capture
(The Eurographics Association, 2022) Xu, Yingyan; Riviere, Jérémy; Zoss, Gaspard; Chandran, Prashanth; Bradley, Derek; Gotardo, Paulo; Pelechano, Nuria; Vanderhaeghe, David
Facial appearance capture techniques estimate geometry and reflectance properties of facial skin by performing a computationally intensive inverse rendering optimization in which one or more images are re-rendered a large number of times and compared to real images coming from multiple cameras. Due to the high computational burden, these techniques often make several simplifying assumptions to tame complexity and make the problem more tractable. For example, it is common to assume that the scene consists of only distant light sources, and ignore indirect bounces of light (on the surface and within the surface). Also, methods based on polarized lighting often simplify the light interaction with the surface and assume perfect separation of diffuse and specular reflectance. In this paper, we move in the opposite direction and demonstrate the impact on facial appearance capture quality when departing from these idealized conditions towards models that seek to more accurately represent the lighting, while at the same time minimally increasing computational burden. We compare the results obtained with a state-of-the-art appearance capture method [RGB*20], with and without our proposed improvements to the lighting model.
An Improved Triangle Encoding Scheme for Cached Tessellation
(The Eurographics Association, 2022) Kerbl, Bernhard; Horváth, Linus; Cornel, Daniel; Wimmer, Michael; Pelechano, Nuria; Vanderhaeghe, David
With the recent advances in real-time rendering that were achieved by embracing software rasterization, the interest in alternative solutions for other fixed-function pipeline stages rises. In this paper, we revisit a recently presented software approach for cached tessellation, which compactly encodes and stores triangles in GPU memory. While the proposed technique is both efficient and versatile, we show that the original encoding is suboptimal and provide an alternative scheme that acts as a drop-in replacement. As shown in our evaluation, the proposed modifications can yield performance gains of 40% and more.
Interactive Facial Expression Editing with Non-linear Blendshape Interpolation
(The Eurographics Association, 2022) Roh, Ji Hyun; Kim, Seong Uk; Jang, Hanyoung; Seol, Yeongho; Kim, Jongmin; Pelechano, Nuria; Vanderhaeghe, David
The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization.
Learning Generic Local Shape Properties for Adaptive Super-Sampling
(The Eurographics Association, 2022) Reinbold, Christian; Westermann, Rüdiger; Pelechano, Nuria; Vanderhaeghe, David
We propose a novel encoder/decoder-based neural network architecture that learns view-dependent shape and appearance of geometry represented by voxel representations. Since the network is trained on local geometry patches, it generalizes to arbitrary models. A geometry model is first encoded into a sparse voxel octree of features learned by a network, and this model representation can then be decoded by another network in-turn for the intended task. We utilize the network for adaptive supersampling in ray-tracing, to predict super-sampling patterns when seeing coarse-scale geometry. We discuss and evaluate the proposed network design, and demonstrate that the decoder network is compact and can be integrated seamlessly into on-chip ray-tracing kernels. We compare the results to previous screen-space super-sampling strategies as well as non-network-based world-space approaches.
Neural Motion Compression with Frequency-adaptive Fourier Feature Network
(The Eurographics Association, 2022) Tojo, Kenji; Chen, Yifei; Umetani, Nobuyuki; Pelechano, Nuria; Vanderhaeghe, David
We present a neural-network-based compression method to alleviate the storage cost of motion capture data. Human motions such as locomotion, often consist of periodic movements. We leverage this periodicity by applying Fourier features to a multilayered perceptron network. Our novel algorithm finds a set of Fourier feature frequencies based on the discrete cosine transformation (DCT) of motion. During training, we incrementally added a dominant frequency of the DCT to a current set of Fourier feature frequencies until a given quality threshold was satisfied. We conducted an experiment using CMU motion dataset, and the results suggest that our method achieves overall high compression ratio while maintaining its quality.
NeuralMLS: Geometry-Aware Control Point Deformation
(The Eurographics Association, 2022) Shechter, Meitar; Hanocka, Rana; Metzer, Gal; Giryes, Raja; Cohen-Or, Daniel; Pelechano, Nuria; Vanderhaeghe, David
We introduce NeuralMLS, a space-based deformation technique, guided by a set of displaced control points. We leverage the power of neural networks to inject the underlying shape geometry into the deformation parameters. The goal of our technique is to enable a realistic and intuitive shape deformation. Our method is built upon moving least-squares (MLS), since it minimizes a weighted sum of the given control point displacements. Traditionally, the influence of each control point on every point in space (i.e., the weighting function) is defined using inverse distance heuristics. In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks. Our geometry-aware control point deformation is agnostic to the surface representation and quality; it can be applied to point clouds or meshes, including non-manifold and disconnected surface soups. We show that our technique facilitates intuitive piecewise smooth deformations, which are well suited for manufactured objects. We show the advantages of our approach compared to existing surface and space-based deformation techniques, both quantitatively and qualitatively.
Procedural Bridges-and-pillars Support Generation
(The Eurographics Association, 2022) Freire, Marco; Hornus, Samuel; Perchy, Salim; Lefebvre, Sylvain; Pelechano, Nuria; Vanderhaeghe, David
Additive manufacturing requires support structures to fabricate parts with overhangs. In this paper, we revisit a known support structure based on bridges-and-pillars (see Figure 1). The support structures are made of vertical pillars supporting horizontal bridges. Their scaffolding structure makes them stable and reliable to print. However, the algorithm heuristic search does not scale well and is prone to produce contacts with the parts, leaving scars after removal. We propose a novel algorithm for this type of supports, focusing on avoiding unnecessary contacts with the part as much as possible. Our approach builds upon example-based model synthesis to enable early detection of collision-free passages as well as non-reachable regions.
Quick Cone Map Generation on the GPU
(The Eurographics Association, 2022) Valasek, Gábor; Bán, Róbert; Pelechano, Nuria; Vanderhaeghe, David
We propose an efficient conservative cone map generation algorithm that has T(N^2 logN) complexity for textures of dimension N ×N in contrast to the T(N^4) complexity of brute-force approaches. This is achieved by using a maximum mip texture of a heightmap to process all texels during the search for cone apertures, resulting in real-time generation times. Furthermore, we show that discarding already visited regions of neighboring mip texels widens the obtained cones considerably while still being conservative. Finally, we present a method to increase cone aperture tangents further at the expense of conservativeness. We compare our methods to brute-force and relaxed cone maps in generation and rendering performance.
Real-Time Path-Guiding Based on Parametric Mixture Models
(The Eurographics Association, 2022) Derevyannykh, Mikhail; Pelechano, Nuria; Vanderhaeghe, David
Path-Guiding algorithms for sampling scattering directions can drastically decrease the variance of Monte Carlo estimators of Light Transport Equation, but their production usage was limited to offline rendering because of memory and computational limitations. We introduce a new robust screen-space technique that is based on online learning of parametric mixture models for guiding the real-time path-tracing algorithm. It requires storing of 8 parameters for every pixel, achieves a reduction of FLIP metric up to 4 times with 1 spp rendering. Also, it consumes less than 1.5ms on RTX 2070 for 1080p and reduces path-tracing timings by generating more coherent rays by about 5% on average. Moreover, it leads to significant bias reduction and a lower level of flickering of SVGF output.
Real-time Sponge and Fluid Simulation
(The Eurographics Association, 2022) Burkus, Viktória; Kárpáti, Attila; Klár, Gergely; Szécsi, László; Pelechano, Nuria; Vanderhaeghe, David
In this paper we present an approach to couple PBD simulation of deformable porous objects with SPH. We propose solutions for simulating the absorption and discharge of fluid by the sponge, and the effect of the fluid on sponge behaviour. We maintain the ability of the original approaches to handle interactions with rigid bodies. Our solution, like PBD in general, is less geared towards physical accuracy, but aims for real-time, visually plausible simulation of these systems, appropriate for interactive VR applications and games.
Resolving Non-Manifoldness on Meshes from Dual Marching Cubes
(The Eurographics Association, 2022) Zint, Daniel; Grosso, Roberto; Gürtler, Philipp; Pelechano, Nuria; Vanderhaeghe, David
There are several methods that reconstruct surfaces from volume data by generating triangle or quad meshes on the dual of the uniform grid. Those methods often provide meshes with better quality than the famous marching cubes. However, they have a common issue: the meshes are not guaranteed to be manifold. We address this issue by presenting a post-processing routine that resolves all non-manifold edges with local refinement. New vertices are positioned on the trilinear interpolant. We verify our method on a wide range of data sets and show that we are capable of resolving all non-manifold issues.
Robust Sample Budget Allocation for MIS
(The Eurographics Association, 2022) Szirmay-Kalos, László; Sbert, Mateu; Pelechano, Nuria; Vanderhaeghe, David
Multiple Importance Sampling (MIS) combines several sampling techniques. Its weighting scheme depends on how many samples are generated with each particular method. This paper examines the optimal determination of the number of samples allocated to each of the combined techniques taking into account that this decision can depend only on a relatively small number of previous samples. The proposed method is demonstrated with the combination of BRDF sampling and Light source sampling, and we show that due to its robustness, it can outperform the theoretically more accurate approaches.
Scene Synthesis with Automated Generation of Textual Descriptions
(The Eurographics Association, 2022) Müller-Huschke, Julian; Ritter, Marcel; Harders, Matthias; Pelechano, Nuria; Vanderhaeghe, David
Most current research on automatically captioning and describing scenes with spatial content focuses on images. We outline that generating descriptive text for a synthesized 3D scene can be achieved via a suitable intermediate representation employed in the synthesis algorithm. As an example, we synthesize scenes of medieval village settings, and generate their descriptions. Our system employs graph grammars, Markov Chain Monte Carlo optimization, and a natural language generation pipeline. Randomly placed objects are evaluated and optimized by a cost function capturing neighborhood relations, path layouts, and collisions. Further, in a pilot study we assess the performance of our framework by comparing the generated descriptions to others provided by human subjects. While the latter were often short and low-effort, the highest-rated ones clearly outperform our generated ones. Nevertheless, the average of all collected human descriptions was indeed rated by the study participants as being less accurate than the automated ones.
Simple Techniques for a Novel Human Body Pose Optimisation Using Differentiable Inverse Rendering
(The Eurographics Association, 2022) Battogtokh, Munkhtulga; Borgo, Rita; Pelechano, Nuria; Vanderhaeghe, David
Human body 3D reconstruction has a wide range of applications including 3D-printing, art, games, and even technical sport analysis. This is a challenging problem due to 2D ambiguity, diversity of human poses, and costs in obtaining multiple views. We propose a novel optimisation scheme that bypasses the prior bias bottleneck and the 2D-pose annotation bottleneck that we identify in single-view reconstruction, and move towards low-resource photo-realistic 3D reconstruction that directly and fully utilises the target image. Our scheme combines domain-specific method SMPLify-X and domain-agnostic inverse rendering method redner, with two simple yet powerful techniques. We demonstrate that our techniques can 1) improve the accuracy of the reconstructed body both qualitatively and quantitatively for challenging inputs, and 2) control optimisation to a selected part only. Our ideas promise extension to more difficult problems and domains even beyond human body reconstruction.

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