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Text-to-3D with Classifier Score Distillation

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arxiv 2310.19415 v2 pith:SWAZG6DX submitted 2023-10-30 cs.CV cs.AIcs.GR

Xin Yu , Yuan-Chen Guo , Yangguang Li , Ding Liang , Song-Hai Zhang , Xiaojuan Qi This is my paper · ORCID
classification cs.CVcs.AIcs.GR
keywords distillationgenerationscoretext-to-3dguidanceclassifierclassifier-freemethods
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. While the usage of classifier-free guidance is well acknowledged to be crucial for successful optimization, it is considered an auxiliary trick rather than the most essential component. In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks. We name this method Classifier Score Distillation (CSD), which can be interpreted as using an implicit classification model for generation. This new perspective reveals new insights for understanding existing techniques. We validate the effectiveness of CSD across a variety of text-to-3D tasks including shape generation, texture synthesis, and shape editing, achieving results superior to those of state-of-the-art methods. Our project page is https://xinyu-andy.github.io/Classifier-Score-Distillation

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  4. LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving

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    LoomNet generates consistent multi-view images from a single input by fusing per-view latent predictions onto shared triplane features before rendering.

  5. SeqTex: Generate Mesh Textures in Video Sequence

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    SeqTex adapts a pretrained video diffusion model to directly generate complete UV texture maps by jointly predicting four multi-view images and the UV map as a five-frame sequence.

  6. DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A pipeline that generates editable 3D scenes from natural language by combining LLM-based layout planning, multi-timestep diffusion distillation, and staged camera sampling.

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