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SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting

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arxiv 2403.05087 v1 pith:N5FRWVFT submitted 2024-03-08 cs.GR cs.CV

classification cs.GRcs.CV
keywords meshgaussianssplattingavatarappearanceavatarsgaussianhumanmotion
verification ladder T0 review T1 audit T2 compute T3 formal
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We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appearance of a virtual human with explicit mesh geometry and implicit appearance modeling with Gaussian Splatting. The Gaussians are defined by barycentric coordinates and displacement on a triangle mesh as Phong surfaces. We extend lifted optimization to simultaneously optimize the parameters of the Gaussians while walking on the triangle mesh. SplattingAvatar is a hybrid representation of virtual humans where the mesh represents low-frequency motion and surface deformation, while the Gaussians take over the high-frequency geometry and detailed appearance. Unlike existing deformation methods that rely on an MLP-based linear blend skinning (LBS) field for motion, we control the rotation and translation of the Gaussians directly by mesh, which empowers its compatibility with various animation techniques, e.g., skeletal animation, blend shapes, and mesh editing. Trainable from monocular videos for both full-body and head avatars, SplattingAvatar shows state-of-the-art rendering quality across multiple datasets.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. StreamME: Simplify 3D Gaussian Avatar within Live Stream

    cs.GR 2025-07 conditional novelty 7.0 of 10

    StreamME reconstructs an animatable head avatar from a live monocular video in about five minutes by attaching 3D Gaussian points to a tracked face mesh and pruning unimportant points during training.

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