Rong Li

Zixuan Xie, Rengan Xie, Rong Li et al.

In this work, we use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs). Without the requirement of complex equipment, our method only takes simple RGB images captured by a drone as inputs to enable physically based and photorealistic novel-view rendering, relighting, and editing. However, a real-world facade usually has complex appearances ranging from diffuse rocks with subtle details to large-area glass windows with specular reflections, making it hard to attend to everything. As a result, previous methods can preserve the geometry details but fail to reconstruct smooth glass windows or verse vise. In order to address this challenge, we introduce three spatial- and semantic-adaptive optimization strategies, including a semantic regularization approach based on zero-shot segmentation techniques to improve material consistency, a frequency-aware geometry regularization to balance surface smoothness and details in different surfaces, and a visibility probe-based scheme to enable efficient modeling of the local lighting in large-scale outdoor environments. In addition, we capture a real-world facade aerial 3D scanning image set and corresponding point clouds for training and benchmarking. The experiment demonstrates the superior quality of our method on facade holistic inverse rendering, novel view synthesis, and scene editing compared to state-of-the-art baselines.

Qifeng Chen, Rengan Xie, Kai Huang et al.

Recently, implicit neural representation has been widely used to generate animatable human avatars. However, the materials and geometry of those representations are coupled in the neural network and hard to edit, which hinders their application in traditional graphics engines. We present a framework for acquiring human avatars that are attached with high-resolution physically-based material textures and triangular mesh from monocular video. Our method introduces a novel information fusion strategy to combine the information from the monocular video and synthesize virtual multi-view images to tackle the sparsity of the input view. We reconstruct humans as deformable neural implicit surfaces and extract triangle mesh in a well-behaved pose as the initial mesh of the next stage. In addition, we introduce an approach to correct the bias for the boundary and size of the coarse mesh extracted. Finally, we adapt prior knowledge of the latent diffusion model at super-resolution in multi-view to distill the decomposed texture. Experiments show that our approach outperforms previous representations in terms of high fidelity, and this explicit result supports deployment on common renderers.