Poster #18 titled "3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting" is displayed at an academic conference. The poster presents research from a collaboration between ETH Zürich, the University of Tübingen, VLG, and the Tübingen AI Center. It details a method for creating animatable clothed human avatars from monocular videos using deformable 3D Gaussian splatting. The overview section summarizes that the technique allows for zero-shot appearances and non-rigid deformations, providing fast novel views synthesis and monocular input. Several logos, including those of ETH Zürich and CVPR, decorate the poster, hinting at its academic and scientific prestige. The methods section outlines the integration of rigid human articulation with non-rigid deformations under the 3DGS framework. It mentions the replacement of Splatted Human Meshes with a local deformation module and a separate MLP to decode appearance. An illustration provides a visual explanation of the method. The experiments section showcases a comparison of generated avatars with various state-of-the-art models, highlighting better quality and faster processing times. The novel view synthesis and novel pose animation sub-sections feature visual results demonstrating the capabilities of the proposed method against other methods such as HumanNeRF and ARAH. Notably, a QR code is present for more information, leading to the project page, and detailed references are provided at the bottom of the poster. A diverse group of attendees can be seen engaging with the research, indicating interest and interaction at this bustling conference venue. Text transcribed from the image: 17 Z ETH Zürich VLG 10 Overview EBERHARD KARLS UNIVERSITAT TUBINGEN Tübingen Al Center 3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting Zhiyin Qian, Shaofei Wang 1,2,3, Marko Mihajlovic', Andreas Geiger2,3, Siyu Tang¹ 1ETH Zürich, 2University of Tübingen, 3Tübingen Al Center https://neuralbodies.github.io/3DGS-Avatar/ MM X N W Input: a monocular video 30 min. training 50 FPS rendering Output: animatable 3D Gaussian avatar Novel view synthesis Novel pose animation Experiments Novel View Synthesis pose-dependent deformation novel pose animation fast training real-time rendering monocular input TL;DR: We create animatable clothed human avatars from monocular videos using 3D Gaussian Splatting [1] pose-dependent non-rigid deformation generalize to unseen poses fast training (less than 30 minutes) real-time rendering (50+ FPS) monocular input Methods Canonical 10 Gaussians (G) t Optimizable SMPL Parameters, Non-rigid Deformation Rigid Transformation T (Sec 4.1) Ta (Sec. 4.2) CVPR Mi SEATTLE, WA JUNE 17-21, 2024 Project Page Novel Pose Animation x x X ✓ x X NeuralBody HumanNeRF ARAH X W x Instant-NVR ✓ NX ✓ InstantAvatar ✓ X X MonoHuman x UV-Volumes X ✓ x DELIFFAS ✓ 3DGS-Avatar (Ours) GT Ours HumanNeRF [4] Training: Inference: 30 min. >8 days 50+ FPS 0.2 FPS ARAH [5] 8 days 0.1 FPS Instant-NVR [6] 5 min. 3 FPS Ours 30 min. 50+ FPS Latest Code Z Direction di PSNR 30.61 29.08 30.24 30.52 30.91 31.02 Color MLP Fe, (Sec. 4.3) SSIM 97.03 96.16 LPIPS 29.58 52.29 96.80 97.09 97.11 97.08 31.73 36.13 28.62 38.40 Ours NB [3] Human NeRF [4] ARAH [5] Ours Instant-NVR [6] ➤ Compared to current state-of-the-arts [4,5], we achieve comparable or even better rendering quality while being hundreds of times faster in both training and inference. ➤ Generalize well to out-of-distribution poses. HumanNeRF [4] >8 days 0.2 FPS ARAH [5] 8 days 0.1 FPS Duff Gaussian Rasterization (E (E45) Observation Space Rendered Image - ΣΣΑΣ ΣΑΣ Σ As-isometric-as-possible Regularization [2] | ➤Integrate rigid human articulation with non-rigid deformation field into the 3DGS framework. Replace spherical harmonics with a local deformation aware MLP to decode color. Apply as-isometric-as-possible regularizations to generate realistic deformation under novel poses. Ablation on AIAP Regularization Full model w/o Lisocov. Lisopos ➤ AIAP regularization enforces consistent movement of the Gaussians, thus removing the artifacts on highly articulated poses. [1] Kerbi et al. 3D Gaussian Splatting for Real-time Radiance Field Rendering. SIGGRAPH, 2023 [2] Killian et al. Geometric Modeling in Shape Space. SIGGRAPH, 2007 [3] Peng et al. Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans. CVPR, 2021 (4] Weng et al. HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video. CVPR, 2022 [5] Wang et al. ARAH: Animatable Volume Rendering of Articulated Human SDFs. ECCV, 2022 [6] Geng et al. Learning Neural Volumetric Representations of Dynamic Humans in Minutes. CVPR, 2023 18