Delighting 3D Assets via Reinforcement Learning

Recently, 3D generation has made progress. However, unwanted highlights in generated textures reduce realism while limiting the editability of 3D assets. Despite this progress, 3D delighting remains an underdeveloped research direction, with existing methods only mitigating lighting as a generation by-product without dedicated evaluation systems or optimization paradigms. In this paper, we highlight the lighting issue in 3D and redefine the delighting paradigm in a comprehensive manner. We first construct several lighting datasets and develop three structurally distinct evaluation models for lighting assessment. Together, they establish a unified evaluation benchmark for 3D delighting optimization. Then, we propose an adaptive decoupled multi-objective Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) joint architecture: SFT injects basic delighting capability, while RL further enhances performance. In experiments, our method improves delighting performance by 18.7% while reducing FID by 55.0%, demonstrating substantially better content consistency. To the best of our knowledge, this paper is the first to introduce an RL paradigm and reward models to the 3D delighting tasks. Code and model weights will be made public after publication.