CrediTex: View-Level Credit Assignment for Human-Aligned 3D Texturing

Despite advances in joint multi-view texture generation, individual views can still contain artifacts, residual illumination, and implausible details. A promising approach is to use post-training with human preference feedback, but existing asset-level rewards obscure quality differences across views and provide the same feedback to views of varying quality. To address this issue, we introduce CrediTex, a post-training framework for 3D texture generation with context-aware per-view rewards and fine-grained credit assignment. First, we construct a dataset of 48K human preference pairs between corresponding views to train the reward model. Given a target-view query, the reward model predicts a continuous scalar reward for the specified view using the reference image and complete view set as context. Subsequently, we compute relative advantages from these rewards across candidates at each fixed viewpoint to guide view-level reinforcement learning. These advantages provide distinct feedback to individual views of the same 3D asset, preserving view-level credit assignment throughout policy optimization. Experiments across multiple models and evaluation metrics demonstrate state-of-the-art performance in both reward modeling and 3D texture generation.