PG: Pacific Graphics Short Papers

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Accelerating Graph-based Path Planning Through Waypoint Clustering
(The Eurographics Association, 2015) Wardhana, Nicholas Mario; Johan, Henry; Seah, Hock-Soon; Stam, Jos and Mitra, Niloy J. and Xu, Kun
Modern Computer Graphics applications commonly feature very large virtual environments and diverse characters which perform different kinds of motions. To accelerate path planning in such scenario, we propose subregion graph data structure. It consists of subregions, which are clusters of locally connected waypoints inside a region, as well as their connectivities. We also present a fast algorithm to automatically generate subregion graph from enhanced waypoint graph map representation, which also supports various motion types and can be created from large virtual environments. Nevertheless, subregion graph can also be generated from any graph-based map representation. Our experiments showed that subregion graph is very compact relative to the input waypoint graph. By firstly planning subregion path, and then limiting waypoint-level planning to the subregion path, up to 8 times average speedup can be achieved, while average length ratios are maintained at as low as 102.5%.
Adaptive Hierarchical Shape Matching
(The Eurographics Association, 2015) Tian, Yuan; Yang, Yin; Guo, Xiaohu; Prabhakaran, Balakrishnan; Stam, Jos and Mitra, Niloy J. and Xu, Kun
In this paper, we present an adaptive hierarchical method allowing users to interact with geometrically complex 3D deformable objects based on an extended shape matching approach. Our method extends the existing multiresolution shape matching methods with improved energy convergence rate. This is achieved by using adaptive integration strategies to avoid insignificant shape matching iterations during the simulation. As demonstrated in our experimental results, the proposed method provides an efficient yet stable deformable simulation of complex models in real-time.
Automatic 3D Posing from 2D Hand-Drawn Sketches
(The Eurographics Association, 2014) Gouvatsos, Alexandros; Xiao, Zhidong; Marsden, Neil; Zhang, Jian J.; John Keyser and Young J. Kim and Peter Wonka
Inferring the 3D pose of a character from a drawing is a non-trivial and under-constrained problem. Solving it may help automate various parts of an animation production pipeline such as pre-visualisation. In this paper, a novel way of inferring the 3D pose from a monocular 2D sketch is proposed. The proposed method does not make any external assumptions about the model, allowing it to be used on different types of characters. The 3D pose inference is formulated as an optimisation problem and a parallel variation of the Particle Swarm Optimisation algorithm called PARAC-LOAPSO is utilised for searching the minimum. Testing in isolation as well as part of a larger scene, the presented method is evaluated by posing a lamp and a horse character. The results show that this method is robust and is able to be extended to various types of models.
Data-Driven Fire Synthesis and Design
(The Eurographics Association, 2014) Wong, Sai-Keung; Chang, Tse-Ching; Ho, Tan-Chi; Chuang, Jung-Hong; John Keyser and Young J. Kim and Peter Wonka
We present a data-driven synthesis approach to design and animate fires in desired shapes and motions. At the preprocessing stage, our system simulates a set of basis fires under specific simulation configurations and stores these basis fires as pathlines in a database. At the design stage, a user sketches a sequence of curves to design the desired shapes of target fires. Then, we compute a subset of basis fires to fit the curves. After that the target fires are synthesized by combining the basis fires. As our method generates target fires along the user sketched curves, our approach enables users to design the fire shapes in an intuitive manner. Experimental results show that our approach can synthesize fires in desired shapes and motions.
Dynamic Skin Deformation Simulation Using Musculoskeletal Model and Soft Tissue Dynamics
(The Eurographics Association, 2016) Murai, Akihiko; Hong, Q. Youn; Yamane, Katsu; Hodgins, Jessica K.; Eitan Grinspun and Bernd Bickel and Yoshinori Dobashi
Deformation of skin and muscle is essential for bringing an animated character to life. This deformation is difficult to animate in a realistic fashion using traditional techniques because of the subtlety of the skin deformations that must move appropriately for the character design. In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation (movement and jiggle) from joint angle data sequences. The algorithm consists of two steps: identification of parameters for a quasi-static muscle model using a musculoskeletal model and a short sequence of skin deformation data, and simulation of dynamic muscle and soft tissue deformation with quasi-static muscle shape and a mass-spring-damper system. We demonstrate our method using skeletal motion capture data of a subject (whose data is not used for training) to create appropriate skin deformations for muscle co-contraction and external impacts. Experimental results show that the simulated skin deformations are quantitatively and qualitatively similar to the measured actual skin deformations.
Generalizable Dynamic Radiance Fields For Talking Head Synthesis With Few-shot
(The Eurographics Association, 2023) Dang, Rujing; Wang, Shaohui; Wang, Haoqian; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
Audio-driven talking head generation has wide applications in virtual games, hosts, online meetings, etc. Recently, great achievements have been made in synthesizing talking heads based on neural radiance fields. However, the existing few-shot talking head synthesis methods still suffer from inaccurate deformation and lack of visual consistency. Therefore, we propose a Generalizable Dynamic Radiance Field (GDRF), which can rapidly generalize to unseen identities with few-shot. We introduce a warping module with 3D constraints to act in feature volume space, which is identity adaptive and exhibits excellent shape-shifting abilities. Our method can generate more accurately deformed and view consistent target images compared to previous methods. Furthermore, we map the audio signal to 3DMM parameters by applying an LSTM network, which helps get long-term context and generate more continuous and natural video. Extensive experiments demonstrate the superiority of our proposed method.
GGAvatar: Dynamic Facial Geometric Adjustment for Gaussian Head Avatar
(The Eurographics Association, 2024) Li, Xinyang; Wang, Jiaxin; Xuan, Yixin; Yao, Gongxin; Pan, Yu; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
Reconstructing animatable 3D head avatars from target subject videos has long been a significant challenge and a hot topic in computer graphics. This paper proposes GGAvatar, a novel 3D avatar representation designed to robustly model dynamic head avatars with complex identities and deformations. GGAvatar employs a coarse-to-fine structure, featuring two core modules: a Neutral Gaussian Initialization Module and a Geometry Morph Adjuster. The Neutral Gaussian Initialization Module pairs Gaussian primitives with deformable triangular meshes, using an adaptive density control strategy to model the geometric structure of the target subject with neutral expressions. The Geometry Morph Adjuster introduces deformation bases for each Gaussian in global space, creating fine-grained low-dimensional representations of deformations to overcome the limitations of the Linear Blend Skinning formula. Extensive experiments show that GGAvatar can produce high-fidelity renderings, outperforming state-of-the-art methods in visual quality and quantitative metrics.
Learning a Style Space for Interactive Line Drawing Synthesis from Animated 3D Models
(The Eurographics Association, 2022) Wang, Zeyu; Wang, Tuanfeng Y.; Dorsey, Julie; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
Most non-photorealistic rendering (NPR) methods for line drawing synthesis operate on a static shape. They are not tailored to process animated 3D models due to extensive per-frame parameter tuning needed to achieve the intended look and natural transition. This paper introduces a framework for interactive line drawing synthesis from animated 3D models based on a learned style space for drawing representation and interpolation. We refer to style as the relationship between stroke placement in a line drawing and its corresponding geometric properties. Starting from a given sequence of an animated 3D character, a user creates drawings for a set of keyframes. Our system embeds the raster drawings into a latent style space after they are disentangled from the underlying geometry. By traversing the latent space, our system enables a smooth transition between the input keyframes. The user may also edit, add, or remove the keyframes interactively, similar to a typical keyframe-based workflow. We implement our system with deep neural networks trained on synthetic line drawings produced by a combination of NPR methods. Our drawing-specific supervision and optimization-based embedding mechanism allow generalization from NPR line drawings to user-created drawings during run time. Experiments show that our approach generates high-quality line drawing animations while allowing interactive control of the drawing style across frames.
Modal Space Subdivision for Physically-plausible 4D Shape Sequence Completion from Sparse Samples
(The Eurographics Association, 2015) Xia, Qing; Li, Shuai; Qin, Hong; Hao, Aimin; Stam, Jos and Mitra, Niloy J. and Xu, Kun
Subdivision techniques are powerful and ubiquitous in generating smooth surfaces from coarse 3D polygonal models, yet space-time subdivision remains unexplored when the goal is to produce dense physically-realistic shape sequence with smooth transition from a set of sparse 3D model samples. In this paper, we considers a completely data-driven strategy and proposes a novel modal space subdivision scheme to facilitate the rapid generation of physically-plausible "in-between" shapes from sparse model samples. The key idea of our approach is to abstract any shape as a point in modal space, represent its potential deformation with shape modes, and iteratively seek all the intermediate shapes between two consecutive models in the modal space via recursive subdivision. Meanwhile, to guarantee deformation details, we further interpolate the shape in the local regions after global modal-space subdivision. Comprehensive experiments on various model inputs have demonstrated the power, versatility, high performance, and potential of our modal space subdivision scheme.
Optimized Route for Crowd Evacuation
(The Eurographics Association, 2016) Wong, Sai-Keung; Wang, Yu-Shuen; Tang, Pao-Kun; Tsai, Tsung-Yu; Eitan Grinspun and Bernd Bickel and Yoshinori Dobashi
An evacuation plan helps people move away from an area or a building. To achieve a fast evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and to maximize the number of evacuees arriving at exits in every time span. Our system considers the crowd distribution, exit locations, and corridor widths when determining the optimal routes. It also simulates crowd movements during the route optimization. To implement this idea, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits nearly at the same time. If this is not the case, our system updates the routes of the slower crowds. Given that crowd simulation is non-linear, the optimal route is computed in an iterative manner. The process repeats until an optimal state is achieved. Experiment results demonstrate the feasibility of our evacuation route optimization.
Real-time Particle Fluid Simulation with WCSPH
(The Eurographics Association, 2012) Zhang, Fengquan; Shen, Xukun; Long, Xiang; Zhao, Bin; Hu, Lei; Chris Bregler and Pedro Sander and Michael Wimmer
In this paper, based on weakly compressible smoothed particles hydrodynamics (WCSPH), we present a method for simulating high quality fluid. We propose a new pressure equation to reduce time overhead, speed up convergence towards small density fluctuations and allow for a larger time step than that of traditional approaches. And based on the new equation, we introduce a new adaptive time-stepping approach that automatically adapts the time step according to each particle current individual time step, thus improving the efficiency of computation and contributing much to a real-time particle fluid simulation. For real-time simulation, we make full use of CPU and GPU in a combined way to build a parallel platform, namely Multi-GPU platform. And optimization of the platform design is made to run a simulation with realistic visual effects in real time.
Robust and Efficient SPH Simulation for High-speed Fluids with the Dynamic Particle Partitioning Method
(The Eurographics Association, 2018) Zheng, Zhong; Gao, Yang; Li, Shuai; Qin, Hong; Hao, Aimin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, our research efforts are devoted to the efficiency issue of the SPH simulation when the ratio of velocities among fluid particles is large. Specifically, we introduce a k-means clustering method into the SPH framework to dynamically partition fluid particles into two disjoint groups based on their velocities, we then use a two-scale time step scheme for these two types of particles. The smaller time steps are for particles with higher speed in order to preserve temporal details and guarantee the numerical stability. In contrast, the larger time steps are used for particles with smaller speeds to reduce the computational expense, and both types of particles are tightly coupled in the simulation.We conduct various experiments which have manifested the advantages of our methods over the conventional SPH technique and its new variants in terms of efficiency and stability.
Video-Realistic Inner Mouth Reanimation
(The Eurographics Association, 2013) Kawai, Masahide; Iwao, Tomoyori; Maejima, Akinobu; Morishima, Shigeo; Bruno Levy and Xin Tong and KangKang Yin
We propose a novel post-effect method that can make an existing speech animation video-realistic by generating an inner mouth appearance that is tailored to the speaker. The automatic generation of photorealistic inner mouth appearances requires only simple inputs and small databases and is not restricted by video lighting. The approach is also applicable for creature speech animation with human voices. Our system uses two key algorithms; one of the algorithms models the inner mouth appearances based on physical assumptions, and the other algorithm synthesizes the inner mouth images with care taken to maintain time continuity and luminance gradients.

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