43-Issue 2

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https://diglib.eg.org/handle/10.2312/3606944

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Real-time Neural Rendering of Dynamic Light Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Coomans, Arno; Dominici, Edoardo Alberto; Döring, Christian; Mueller, Joerg H.; Hladky, Jozef; Steinberger, Markus; Bermano, Amit H.; Kalogerakis, Evangelos
Synthesising high-quality views of dynamic scenes via path tracing is prohibitively expensive. Although caching offline-quality global illumination in neural networks alleviates this issue, existing neural view synthesis methods are limited to mainly static scenes, have low inference performance or do not integrate well with existing rendering paradigms. We propose a novel neural method that is able to capture a dynamic light field, renders at real-time frame rates at 1920x1080 resolution and integrates seamlessly with Monte Carlo ray tracing frameworks. We demonstrate how a combination of spatial, temporal and a novel surface-space encoding are each effective at capturing different kinds of spatio-temporal signals. Together with a compact fully-fused neural network and architectural improvements, we achieve a twenty-fold increase in network inference speed compared to related methods at equal or better quality. Our approach is suitable for providing offline-quality real-time rendering in a variety of scenarios, such as free-viewpoint video, interactive multi-view rendering, or streaming rendering. Finally, our work can be integrated into other rendering paradigms, e.g., providing a dynamic background for interactive scenarios where the foreground is rendered with traditional methods.
Stylize My Wrinkles: Bridging the Gap from Simulation to Reality
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Weiss, Sebastian; Stanhope, Jackson; Chandran, Prashanth; Zoss, Gaspard; Bradley, Derek; Bermano, Amit H.; Kalogerakis, Evangelos
Modeling realistic human skin with pores and wrinkles down to the milli- and micrometer resolution is a challenging task. Prior work showed that such micro geometry can be efficiently generated through simulation methods, or in specialized cases via 3D scanning of real skin. Simulation methods allow to highly customize the wrinkles on the face, but can lead to a synthetic look. Scanning methods can lead to a more organic look for the micro details, however these methods are only applicable to small skin patches due to the required image resolution. In this work we aim to overcome the gap between synthetic simulation and real skin scanning, by proposing a method that can be applied to large skin regions (e.g. an entire face) with the controllability of simulation and the organic look of real micro details. Our method is based on style transfer at its core, where we use scanned displacement maps of real skin patches as style images and displacement maps from an artist-friendly simulation method as content images. We build a library of displacement maps as style images by employing a simplified scanning setup that can capture high-resolution patches of real skin. To create the content component for the style transfer and to facilitate parameter-tuning for the simulation, we design a library of preset parameter values depicting different skin types, and present a new method to fit the simulation parameters to scanned skin patches. This allows fully-automatic parameter generation, interpolation and stylization across entire faces. We evaluate our method by generating realistic skin micro details for various subjects of different ages and genders, and demonstrate that our approach achieves a more organic and natural look than simulation alone.
CharacterMixer: Rig-Aware Interpolation of 3D Characters
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhan, Xiao; Fu, Rao; Ritchie, Daniel; Bermano, Amit H.; Kalogerakis, Evangelos
We present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMixer also enables interpolation during motion for such characters, a novel feature. Interpolation is an important shape editing operation, but prior methods have limitations when applied to rigged characters: they either ignore the rig (making interpolated characters no longer posable) or use a fixed rig and mesh topology. To handle different mesh topologies, CharacterMixer uses a signed distance field (SDF) representation of character shapes, with one SDF per bone. To handle different skeleton topologies, it computes a hierarchical correspondence between source and target character skeletons and interpolates the SDFs of corresponding bones. This correspondence also allows the creation of a single ''unified skeleton'' for posing and animating interpolated characters. We show that CharacterMixer produces qualitatively better interpolation results than two state-of-the-art methods while preserving a rig throughout interpolation. Project page: https://seanxzhan.github.io/projects/CharacterMixer.
Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Rasoulzadeh, Shervin; Wimmer, Michael; Stauss, Philipp; Kovacic, Iva; Bermano, Amit H.; Kalogerakis, Evangelos
We present Strokes2Surface, an offline geometry reconstruction pipeline that recovers well-connected curve networks from imprecise 4D sketches to bridge concept design and digital modeling stages in architectural design. The input to our pipeline consists of 3D strokes' polyline vertices and their timestamps as the 4th dimension, along with additional metadata recorded throughout sketching. Inspired by architectural sketching practices, our pipeline combines a classifier and two clustering models to achieve its goal. First, with a set of extracted hand-engineered features from the sketch, the classifier recognizes the type of individual strokes between those depicting boundaries (Shape strokes) and those depicting enclosed areas (Scribble strokes). Next, the two clustering models parse strokes of each type into distinct groups, each representing an individual edge or face of the intended architectural object. Curve networks are then formed through topology recovery of consolidated Shape clusters and surfaced using Scribble clusters guiding the cycle discovery. Our evaluation is threefold: We confirm the usability of the Strokes2Surface pipeline in architectural design use cases via a user study, we validate our choice of features via statistical analysis and ablation studies on our collected dataset, and we compare our outputs against a range of reconstructions computed using alternative methods.
PossibleImpossibles: Exploratory Procedural Design of Impossible Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Yuanbo; Ma, Tianyi; Aljumayaat, Zaineb; Ritchie, Daniel; Bermano, Amit H.; Kalogerakis, Evangelos
We present a method for generating structures in three-dimensional space that appear to be impossible when viewed from specific perspectives. Previous approaches focus on helping users to edit specific structures and require users to have knowledge of structural positioning causing the impossibility. On the contrary, our system is designed to aid users without prior knowledge to explore a wide range of potentially impossible structures. The essence of our method lies in features we call visual bridges that confuse viewers regarding the depth of the resulting structure. We use these features as starting points and employ procedural modeling to systematically generate the result. We propose scoring functions for enforcing desirable spatial arrangement of the result and use Sequential Monte Carlo to sample outputs that score well under these functions. We also present a proof-ofconcept user interface and demonstrate various results generated using our system.
DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced 3-Point Trackers
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Yang, Dongseok; Kang, Jiho; Ma, Lingni; Greer, Joseph; Ye, Yuting; Lee, Sung-Hee; Bermano, Amit H.; Kalogerakis, Evangelos
Full-body avatar presence is important for immersive social and environmental interactions in digital reality. However, current devices only provide three six degrees of freedom (DOF) poses from the headset and two controllers (i.e. three-point trackers). Because it is a highly under-constrained problem, inferring full-body pose from these inputs is challenging, especially when supporting the full range of body proportions and use cases represented by the general population. In this paper, we propose a deep learning framework, DivaTrack, which outperforms existing methods when applied to diverse body sizes and activities. We augment the sparse three-point inputs with linear accelerations from Inertial Measurement Units (IMU) to improve foot contact prediction. We then condition the otherwise ambiguous lower-body pose with the predictions of foot contact and upper-body pose in a two-stage model. We further stabilize the inferred full-body pose in a wide range of configurations by learning to blend predictions that are computed in two reference frames, each of which is designed for different types of motions. We demonstrate the effectiveness of our design on a large dataset that captures 22 subjects performing challenging locomotion for three-point tracking, including lunges, hula-hooping, and sitting. As shown in a live demo using the Meta VR headset and Xsens IMUs, our method runs in real-time while accurately tracking a user's motion when they perform a diverse set of movements.
Cinematographic Camera Diffusion Model
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Jiang, Hongda; Wang, Xi; Christie, Marc; Liu, Libin; Chen, Baoquan; Bermano, Amit H.; Kalogerakis, Evangelos
Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization-based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer-based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high-level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text-to-camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control.
Enhancing Image Quality Prediction with Self-supervised Visual Masking
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Çogalan, Ugur; Bemana, Mojtaba; Seidel, Hans-Peter; Myszkowski, Karol; Bermano, Amit H.; Kalogerakis, Evangelos
Full-reference image quality metrics (FR-IQMs) aim to measure the visual differences between a pair of reference and distorted images, with the goal of accurately predicting human judgments. However, existing FR-IQMs, including traditional ones like PSNR and SSIM and even perceptual ones such as HDR-VDP, LPIPS, and DISTS, still fall short in capturing the complexities and nuances of human perception. In this work, rather than devising a novel IQM model, we seek to improve upon the perceptual quality of existing FR-IQM methods. We achieve this by considering visual masking, an important characteristic of the human visual system that changes its sensitivity to distortions as a function of local image content. Specifically, for a given FR-IQM metric, we propose to predict a visual masking model that modulates reference and distorted images in a way that penalizes the visual errors based on their visibility. Since the ground truth visual masks are difficult to obtain, we demonstrate how they can be derived in a self-supervised manner solely based on mean opinion scores (MOS) collected from an FR-IQM dataset. Our approach results in enhanced FR-IQM metrics that are more in line with human prediction both visually and quantitatively.
OptFlowCam: A 3D-Image-Flow-Based Metric in Camera Space for Camera Paths in Scenes with Extreme Scale Variations
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Piotrowski, Lisa; Motejat, Michael; Rössl, Christian; Theisel, Holger; Bermano, Amit H.; Kalogerakis, Evangelos
Interpolation between camera positions is a standard problem in computer graphics and can be considered the foundation of camera path planning. As the basis for a new interpolation method, we introduce a new Riemannian metric in camera space, which measures the 3D image flow under a small movement of the camera. Building on this, we define a linear interpolation between two cameras as shortest geodesic in camera space, for which we provide a closed-form solution after a mild simplification of the metric. Furthermore, we propose a geodesic Catmull-Rom interpolant for keyframe camera animation. We compare our approach with several standard camera interpolation methods and obtain consistently better camera paths especially for cameras with extremely varying scales.
Hierarchical Co-generation of Parcels and Streets in Urban Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Chen, Zebin; Song, Peng; Ortner, F. Peter; Bermano, Amit H.; Kalogerakis, Evangelos
We present a computational framework for modeling land parcels and streets. In the real world, parcels and streets are highly coupled with each other since a street network connects all the parcels in a certain area. However, existing works model parcels and streets separately to simplify the problem, resulting in urban layouts with irregular parcels and/or suboptimal streets. In this paper, we propose a hierarchical approach to co-generate parcels and streets from a user-specified polygonal land shape, guided by a set of fundamental urban design requirements. At each hierarchical level, new parcels are generated based on binary splitting of existing parcels, and new streets are subsequently generated by leveraging efficient graph search tools to ensure that each new parcel has a street access. At the end, we optimize the geometry of the generated parcels and streets to further improve their geometric quality. Our computational framework outputs an urban layout with a desired number of regular parcels that are reachable via a connected street network, for which users are allowed to control the modeling process both locally and globally. Quantitative comparisons with state-of-the-art approaches show that our framework is able to generate parcels and streets that are superior in some aspects.
Neural Denoising for Deep-Z Monte Carlo Renderings
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhang, Xianyao; Röthlin, Gerhard; Zhu, Shilin; Aydin, Tunç Ozan; Salehi, Farnood; Gross, Markus; Papas, Marios; Bermano, Amit H.; Kalogerakis, Evangelos
We present a kernel-predicting neural denoising method for path-traced deep-Z images that facilitates their usage in animation and visual effects production. Deep-Z images provide enhanced flexibility during compositing as they contain color, opacity, and other rendered data at multiple depth-resolved bins within each pixel. However, they are subject to noise, and rendering until convergence is prohibitively expensive. The current state of the art in deep-Z denoising yields objectionable artifacts, and current neural denoising methods are incapable of handling the variable number of depth bins in deep-Z images. Our method extends kernel-predicting convolutional neural networks to address the challenges stemming from denoising deep-Z images. We propose a hybrid reconstruction architecture that combines the depth-resolved reconstruction at each bin with the flattened reconstruction at the pixel level. Moreover, we propose depth-aware neighbor indexing of the depth-resolved inputs to the convolution and denoising kernel application operators, which reduces artifacts caused by depth misalignment present in deep-Z images. We evaluate our method on a production-quality deep-Z dataset, demonstrating significant improvements in denoising quality and performance compared to the current state-of-the-art deep-Z denoiser. By addressing the significant challenge of the cost associated with rendering path-traced deep-Z images, we believe that our approach will pave the way for broader adoption of deep-Z workflows in future productions.
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Liang, Hanxue; Wu, Tianhao; Hanji, Param; Banterle, Francesco; Gao, Hongyun; Mantiuk, Rafal; Öztireli, Cengiz; Bermano, Amit H.; Kalogerakis, Evangelos
Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research on how NVS methods perform with respect to perceived video quality. We present the first study on perceptual evaluation of NVS and NeRF variants. For this study, we collected two datasets of scenes captured in a controlled lab environment as well as in-the-wild. In contrast to existing datasets, these scenes come with reference video sequences, allowing us to test for temporal artifacts and subtle distortions that are easily overlooked when viewing only static images. We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics. We present a detailed analysis of the results and recommendations for dataset and metric selection for NVS evaluation.
Predicting Perceived Gloss: Do Weak Labels Suffice?
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Guerrero-Viu, Julia; Subias, Jose Daniel; Serrano, Ana; Storrs, Katherine R.; Fleming, Roland W.; Masia, Belen; Gutierrez, Diego; Bermano, Amit H.; Kalogerakis, Evangelos
Estimating perceptual attributes of materials directly from images is a challenging task due to their complex, not fullyunderstood interactions with external factors, such as geometry and lighting. Supervised deep learning models have recently been shown to outperform traditional approaches, but rely on large datasets of human-annotated images for accurate perception predictions. Obtaining reliable annotations is a costly endeavor, aggravated by the limited ability of these models to generalise to different aspects of appearance. In this work, we show how a much smaller set of human annotations (''strong labels'') can be effectively augmented with automatically derived ''weak labels'' in the context of learning a low-dimensional image-computable gloss metric. We evaluate three alternative weak labels for predicting human gloss perception from limited annotated data. Incorporating weak labels enhances our gloss prediction beyond the current state of the art. Moreover, it enables a substantial reduction in human annotation costs without sacrificing accuracy, whether working with rendered images or real photographs.
Physically-based Analytical Erosion for fast Terrain Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Tzathas, Petros; Gailleton, Boris; Steer, Philippe; Cordonnier, Guillaume; Bermano, Amit H.; Kalogerakis, Evangelos
Terrain generation methods have long been divided between procedural and physically-based. Procedural methods build upon the fast evaluation of a mathematical function but suffer from a lack of geological consistency, while physically-based simulation enforces this consistency at the cost of thousands of iterations unraveling the history of the landscape. In particular, the simulation of the competition between tectonic uplift and fluvial erosion expressed by the stream power law raised recent interest in computer graphics as this allows the generation and control of consistent large-scale mountain ranges, albeit at the cost of a lengthy simulation. In this paper, we explore the analytical solutions of the stream power law and propose a method that is both physically-based and procedural, allowing fast and consistent large-scale terrain generation. In our approach, time is no longer the stopping criterion of an iterative process but acts as the parameter of a mathematical function, a slider that controls the aging of the input terrain from a subtle erosion to the complete replacement by a fully formed mountain range. While analytical solutions have been proposed by the geomorphology community for the 1D case, extending them to a 2D heightmap proves challenging. We propose an efficient implementation of the analytical solutions with a multigrid accelerated iterative process and solutions to incorporate landslides and hillslope processes – two erosion factors that complement the stream power law.
Navigating the Manifold of Translucent Appearance
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lanza, Dario; Masia, Belen; Jarabo, Adrian; Bermano, Amit H.; Kalogerakis, Evangelos
We present a perceptually-motivated manifold for translucent appearance, designed for intuitive editing of translucent materials by navigating through the manifold. Classic tools for editing translucent appearance, based on the use of sliders to tune a number of parameters, are challenging for non-expert users: These parameters have a highly non-linear effect on appearance, and exhibit complex interplay and similarity relations between them. Instead, we pose editing as a navigation task in a low-dimensional space of appearances, which abstracts the user from the underlying optical parameters. To achieve this, we build a low-dimensional continuous manifold of translucent appearance that correlates with how humans perceive this type of materials. We first analyze the correlation of different distance metrics in image space with human perception. We select the best-performing metric to build a low-dimensional manifold, which can be used to navigate the space of translucent appearance. To evaluate the validity of our proposed manifold within its intended application scenario, we build an editing interface that leverages the manifold, and relies on image navigation plus a fine-tuning step to edit appearance. We compare our intuitive interface to a traditional, slider-based one in a user study, demonstrating its effectiveness and superior performance when editing translucent objects.
Unfolding via Mesh Approximation using Surface Flows
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zawallich, Lars; Pajarola, Renato; Bermano, Amit H.; Kalogerakis, Evangelos
Manufacturing a 3D object by folding from a 2D material is typically done in four steps: 3D surface approximation, unfolding the surface into a plane, printing and cutting the outline of the unfolded shape, and refolding it to a 3D object. Usually, these steps are treated separately from each other. In this work we jointly address the first two pipeline steps by allowing the 3D representation to smoothly change while unfolding. This way, we increase the chances to overcome possible ununfoldability issues. To join the two pipeline steps, our work proposes and combines different surface flows with a Tabu Unfolder. We empirically investigate the effects that different surface flows have on the performance as well as on the quality of the unfoldings. Additionally, we demonstrate the ability to solve cases by approximation which comparable algorithms either have to segment or can not solve at all.
Volcanic Skies: Coupling Explosive Eruptions with Atmospheric Simulation to Create Consistent Skyscapes
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Pretorius, Pieter C.; Gain, James; Lastic, Maud; Cordonnier, Guillaume; Chen, Jiong; Rohmer, Damien; Cani, Marie-Paule; Bermano, Amit H.; Kalogerakis, Evangelos
Explosive volcanic eruptions rank among the most terrifying natural phenomena, and are thus frequently depicted in films, games, and other media, usually with a bespoke once-off solution. In this paper, we introduce the first general-purpose model for bi-directional interaction between the atmosphere and a volcano plume. In line with recent interactive volcano models, we approximate the plume dynamics with Lagrangian disks and spheres and the atmosphere with sparse layers of 2D Eulerian grids, enabling us to focus on the transfer of physical quantities such as temperature, ash, moisture, and wind velocity between these sub-models. We subsequently generate volumetric animations by noise-based procedural upsampling keyed to aspects of advection, convection, moisture, and ash content to generate a fully-realized volcanic skyscape. Our model captures most of the visually salient features emerging from volcano-sky interaction, such as windswept plumes, enmeshed cap, bell and skirt clouds, shockwave effects, ash rain, and sheathes of lightning visible in the dark.
Freeform Shape Fabrication by Kerfing Stiff Materials
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Speetzen, Nils; Kobbelt, Leif; Bermano, Amit H.; Kalogerakis, Evangelos
Fast, flexible, and cost efficient production of 3D models from 2D material sheets is a key component in digital fabrication and prototyping. In order to achieve high quality approximations of freeform shapes, a common set of methods aim to produce bendable 2D cutouts that are then assembled. So far bent surfaces are achieved automatically by computing developable patches of the input surface, e.g. in the context of papercraft. For stiff materials such as medium-density fibreboard (MDF) or plywood, the 2D cutouts require the application of additional cutting patterns (''kerfing'') to make them bendable. Such kerf patterns are commonly constructed with considerable user input, e.g. in architectural design. We propose a fully automatic method that produces kerfed cutouts suitable for the assembly of freeform shapes from stiff material sheets. By exploring the degrees of freedom emerging from the choice of bending directions, the creation of box joints at the patch boundaries as well as the application of kerf cuts with adaptive density, our method is able to achieve a high quality approximation of the input.
Computational Smocking through Fabric-Thread Interaction
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhou, Ningfeng; Ren, Jing; Sorkine-Hornung, Olga; Bermano, Amit H.; Kalogerakis, Evangelos
We formalize Italian smocking, an intricate embroidery technique that gathers flat fabric into pleats along meandering lines of stitches, resulting in pleats that fold and gather where the stitching veers. In contrast to English smocking, characterized by colorful stitches decorating uniformly shaped pleats, and Canadian smocking, which uses localized knots to form voluminous pleats, Italian smocking permits the fabric to move freely along the stitched threads following curved paths, resulting in complex and unpredictable pleats with highly diverse, irregular structures, achieved simply by pulling on the threads. We introduce a novel method for digital previewing of Italian smocking results, given the thread stitching path as input. Our method uses a coarse-grained mass-spring system to simulate the interaction between the threads and the fabric. This configuration guides the fine-level fabric deformation through an adaptation of the state-of-the-art simulator, C-IPC [LKJ21]. Our method models the general problem of fabric-thread interaction and can be readily adapted to preview Canadian smocking as well.We compare our results to baseline approaches and physical fabrications to demonstrate the accuracy of our method.
Learning to Stabilize Faces
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Bednarik, Jan; Wood, Erroll; Choutas, Vassilis; Bolkart, Timo; Wang, Daoye; Wu, Chenglei; Beeler, Thabo; Bermano, Amit H.; Kalogerakis, Evangelos
Nowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further processing: we need to stabilize them to remove unwanted head movement. Stabilization is important for tasks like game development or movie making which require facial expressions to be cleanly separated from rigid head motion. Since manual stabilization is labor-intensive, there have been attempts to automate it. However, previous methods remain impractical: they either still require some manual input, produce imprecise alignments, rely on dubious heuristics and slow optimization, or assume a temporally ordered input. Instead, we present a new learning-based approach that is simple and fully automatic. We treat stabilization as a regression problem: given two face meshes, our network directly predicts the rigid transform between them that brings their skulls into alignment. We generate synthetic training data using a 3D Morphable Model (3DMM), exploiting the fact that 3DMM parameters separate skull motion from facial skin motion. Through extensive experiments we show that our approach outperforms the state-of-the-art both quantitatively and qualitatively on the tasks of stabilizing discrete sets of facial expressions as well as dynamic facial performances. Furthermore, we provide an ablation study detailing the design choices and best practices to help others adopt our approach for their own uses.

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