Text-to-3D with Classifier Score Distillation

However, it is still challenging and expensive to create a high-quality 3D asset as it requires a high level of expertise. Therefore, automating this process with generative models has become an important problem, which remains challenging due to the scarcity of data and the complexity of 3D representations. Recently, techniques based on Score Distillation Sampling (SDS) (Poole et al., 2022; Lin et al., 2023; Chen et al., 2023; Wang et al., 2023b), also known as Score Jacobian Chaining (SJC) (Wang et al., 2023a), have emerged as a major research direction for text-to-3D generation, as they can produce high-quality and intricate 3D results from diverse text prompts without requiring 3D data for training. The core principle behind SDS is to optimize 3D representations by encouraging their rendered images to move towards high probability density regions conditioned on the text, where the supervision is provided by a pre-trained 2D diffusion model (Ho et al., 2020; Sohl-Dickstein et al., 2015; Rombach et al., 2022; Saharia et al., 2022; Balaji et al., 2022). DreamFusion (Poole et al., 2022) advocates the use of SDS for the optimization of Neural Radiance Fields (NeRF).