AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation

Score Distillation Sampling and its variants have shown strong potential in text-to-3D generation by leveraging scores estimated from pretrained text-to-image diffusion models to optimize 3D representations. However, due to the view-agnostic nature of these scores, existing methods often suffer from the multi-face Janus problem, leading to inconsistencies across different views. In this work, we propose Rectified Score Distillation, which addresses this issue by incorporating view-conditioned scores as priors. Specifically, we formulate a reverse ordinary differential equation (ODE) that is additionally conditioned on camera poses. Then we rectify the standard, view-irrelevant scores to approximate the desired gradients along this ODE. Building on these rectified scores, our full framework, named AgonicDreamer, enables the generation of photorealistic and multi-view consistent 3D content with fine-grained details, as validated by extensive experimental results.