Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading

Reconstructing high-fidelity, relightable 3D avatars from asingle in-the-wild image is a challenging ill-posed problem, primarilyhindered by the scarcity of high-quality PBR data and the complex-ity of disentangling illumination from intrinsic materials. In this paper,we present a data-efficient framework that leverages the robust priorsof a unified pre-trained diffusion backbone to sequentially address tex-ture completion, delighting, and material decomposition. Unlike exist-ing methods that rely on fragmented pipelines or extensive proprietarydatasets, we utilize cascaded Low-Rank Adaptations (LoRAs) to adaptthe strong generative prior of the diffusion model for each sub-task inUV space. Specifically, we first employ an Inpainting LoRA to completemissing UV textures caused by occlusion, leveraging the model’s seman-tic understanding to generate semantically and photometrically coher-ent details. Subsequently, a Light-Homogenization LoRA and a novelCross-Intrinsic Attention mechanism are introduced to remove baked-inlighting and collaboratively synthesize pixel-aligned PBR maps (Albedo,Normal, Roughness, Specular, and Displacement). To ensure physicalplausibility, we impose a UV-space differentiable BRDF shading lossduring the decomposition stage, forcing the generative process to adhereto the rendering equation without the artifacts typical of rasterization-based supervision. Extensive experiments demonstrate that our method,trained on fewer than 100 real 3D scans, generates comprehensive, 4K-resolution PBR assets with superior realism and generalization comparedto state-of-the-art methods, and all training code and model weights willbe released upon acceptance.