44-Issue 6

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https://diglib.eg.org/handle/10.2312/3607243

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Browsing 44-Issue 6 by Subject "animation"

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    3DGM: Deformable and Texturable 3D Gaussian Model via Level-of-Detail Proxy
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Xiangzhi Eric; Sin, Zackary P. T.; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
    3D Gaussian Splatting has markedly impacted neural rendering by achieving impressive fidelity and performance. Despite this achievement, it is not readily applicable to developing interactive applications. Real-time applications like XR apps and games require functions such as animation, UV mapping and level of detail (LOD) simultaneously manipulated through a 3D model. To address this need, we propose a modelling strategy analogous to typical 3D models, which we call 3D Gaussian Model (3DGM). 3DGM relies on attaching 3D Gaussians on the triangles of a mesh proxy, and the key idea is to bind sheared 3D Gaussians in texture space and re-projecting them back to world space through implicit shell mapping; this design naturally enables deformation and UV mapping via the proxy. Further, to optimize speed and fidelity based on different viewing distances, each triangle can be tessellated to change the number of involved 3D Gaussians adaptively. Application-wise, we will show that our proxy-based 3DGM is capable of enabling novel deformation without animated training data, texture transferring via UV mapping of the 3D Gaussians, and LOD rendering. The results indicate that our model achieves better fidelity for deformation and better optimization of fidelity and performance given different viewing distances. Further, we believe the results indicate the potential of our work for enabling interactive applications for 3D Gaussian Splatting.
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    Herds From Video: Learning a Microscopic Herd Model From Macroscopic Motion Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Gong, Xianjin; Gain, James; Rohmer, Damien; Lyonnet, Sixtine; Pettré, Julien; Cani, Marie-Paule; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
    We present a method for animating herds that automatically tunes a microscopic herd model based on a short video clip of real animals. Our method handles videos with dense herds, where individual animal motion cannot be separated out. Our contribution is a novel framework for extracting macroscopic herd behaviour from such video clips, and then deriving the microscopic agent parameters that best match this behaviour. To support this learning process, we extend standard agent models to provide a separation between leaders and followers, better match the occlusion and field-of-view limitations of real animals, support differentiable parameter optimization and improve authoring control. We validate the method by showing that once optimized, the social force and perception parameters of the resulting herd model are accurate enough to predict subsequent frames in the video, even for macroscopic properties not directly incorporated in the optimization process. Furthermore, the extracted herding characteristics can be applied to any terrain with a palette and region-painting approach that generalizes to different herd sizes and leader trajectories. This enables the authoring of herd animations in new environments while preserving learned behaviour.
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    LEAD: Latent Realignment for Human Motion Diffusion
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Andreou, Nefeli; Wang, Xi; Fernández Abrevaya, Victoria; Cani, Marie-Paule; Chrysanthou, Yiorgos; Kalogeiton, Vicky; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
    Our goal is to generate realistic human motion from natural language. Modern methods often face a trade-off between model expressiveness and text-to-motion (T2M) alignment. Some align text and motion latent spaces but sacrifice expressiveness; others rely on diffusion models producing impressive motions but lacking semantic meaning in their latent space. This may compromise realism, diversity and applicability. Here, we address this by combining latent diffusion with a realignment mechanism, producing a novel, semantically structured space that encodes the semantics of language. Leveraging this capability, we introduce the task of textual motion inversion to capture novel motion concepts from a few examples. For motion synthesis, we evaluate LEAD on HumanML3D and KIT-ML and show comparable performance to the state-of-the-art in terms of realism, diversity and textmotion consistency. Our qualitative analysis and user study reveal that our synthesised motions are sharper, more human-like and comply better with the text compared to modern methods. For motion textual inversion (MTI), our method demonstrates improvements in capturing out-of-distribution characteristics in comparison to traditional VAEs.
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    MPACT: Mesoscopic Profiling and Abstraction of Crowd Trajectories
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Lemonari, Marilena; Panayiotou, Andreas; Kyriakou, Theodoros; Pelechano, Nuria; Chrysanthou, Yiorgos; Aristidou, Andreas; Charalambous, Panayiotis; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
    Simulating believable crowds for applications like movies or games is challenging due to the many components that comprise a realistic outcome. Users typically need to manually tune a large number of simulation parameters until they reach the desired results. We introduce MPACT, a framework that leverages image-based encoding to convert unlabelled crowd data into meaningful and controllable parameters for crowd generation. In essence, we train a parameter prediction network on a diverse set of synthetic data, which includes pairs of images and corresponding crowd profiles. The learned parameter space enables: (a) implicit crowd authoring and control, allowing users to define desired crowd scenarios using real-world trajectory data, and (b) crowd analysis, facilitating the identification of crowd behaviours in the input and the classification of unseen scenarios through operations within the latent space. We quantitatively and qualitatively evaluate our framework, comparing it against real-world data and selected baselines, while also conducting user studies with expert and novice users. Our experiments show that the generated crowds score high in terms of simulation believability, plausibility and crowd behaviour faithfulness.