TE-NeRF: Triplane-Enhanced Neural Radiance Field for Artifact-Free Human Rendering

Sadia Mubashshira, Kevin Desai

Abstract: Rendering high-fidelity human avatars from monocular video with accurate surface details is crucial for applications in virtual reality, digital entertainment, and telepresence. While neural radiance field (NeRF) based models have shown promise in capturing human body representations, they often fail to render fine-grained details, such as cloth wrinkles and facial contours, resulting in noticeable artifacts outside the human body that undermine realism. Existing approaches lack the capability to achieve both detailed texture representation and seamless surface geometry. To address these limitations, we propose TE-NeRF, an enhanced framework that integrates Triplane features to improve detail accuracy and reduce artifacts. By associating Triplane-based features with each SMPL vertex and processing them through a density MLP, our method achieves precise representation of texture and geometry. An adaptive weight blending mechanism dynamically combines vertex-specific and ray-sampled densities, enabling a balance between detail preservation and smoothness in rendering. Additionally, a silhouette loss is introduced to reinforce alignment, particularly in complex regions like clothing edges and facial contours. Our approach reduces rendering artifacts compared to state-of-the-art methods, though with a slight tradeoff in cloth detail sharpness, resulting in visually coherent human renderings validated through extensive experiments.

External IDs:dblp:conf/wacv/MubashshiraD25