Fine-grained information transfer: Details Refinement module for Diffusion based 3D Generation model

One key limitation of diffusion-based 3D asset generation methods such as TRELLIS and Zero-1-to-3 is their difficulty in preserving fine-grained texture and structural details when synthesizing novel views. To address this, I drew inspiration from optical-flow estimation in Video Frame Interpolation (VFI) and developed a refinement module that transfers fine-detail information from the original input image to diffusion-generated novel-view outputs in feature space. The module integrates feature matching and attention-based fusion, built upon a VFI pipeline to guide detail-preserving correspondence and refinement. As a result, my approach effectively restores high-frequency details in novel views and potentially leads downstream applications such as reconstructing fine-detailed 3D models from a single image.

During co-op as a researcher at Noah's Ark Lab