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MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos

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arxiv 2407.08414 v1 pith:44Q4ASCG submitted 2024-07-11 cs.CV cs.GR

Yushuo Chen , Zerong Zheng , Zhe Li , Chao Xu , Yebin Liu This is my paper · ORCID
classification cs.CVcs.GR
keywords triangularavataravatarshigh-qualitylearningeditingfieldgeometry
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We present a novel pipeline for learning high-quality triangular human avatars from multi-view videos. Recent methods for avatar learning are typically based on neural radiance fields (NeRF), which is not compatible with traditional graphics pipeline and poses great challenges for operations like editing or synthesizing under different environments. To overcome these limitations, our method represents the avatar with an explicit triangular mesh extracted from an implicit SDF field, complemented by an implicit material field conditioned on given poses. Leveraging this triangular avatar representation, we incorporate physics-based rendering to accurately decompose geometry and texture. To enhance both the geometric and appearance details, we further employ a 2D UNet as the network backbone and introduce pseudo normal ground-truth as additional supervision. Experiments show that our method can learn triangular avatars with high-quality geometry reconstruction and plausible material decomposition, inherently supporting editing, manipulation or relighting operations.

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Cited by 4 Pith papers

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

  1. Generative Relightable Avatars

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    GRA combines UV-space material optimization and physics rendering with feed-forward texture refinement and a fine-tuned video-to-video diffusion model to achieve controllable, high-detail relighting of full-body avatars.

  2. Real-time High-fidelity Gaussian Human Avatars with Position-based Interpolation of Spatially Distributed MLPs

    cs.GR 2025-04 conditional novelty 6.0 of 10

    Gaussian-splat avatars reach 166 fps with finer details by replacing a large CNN with spatially distributed pose-only MLPs whose interpolated coefficients drive per-Gaussian offset bases.

  3. AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    AniGS produces an animatable 3D avatar from a single image by synthesizing multi-view canonical images and normals with a video diffusion model and reconstructing them via 4D Gaussian Splatting.

  4. RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians

    cs.CV 2025-01 conditional novelty 5.0 of 10

    RMAvatar couples mesh-embedded Gaussian splats with a pose-conditioned rectification MLP to reconstruct animatable clothed avatars from monocular video.

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