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Explore the frontier of 3D generation: how 2D diffusion models can be distilled into 3D representations via Score Distillation Sampling, how single images are lifted to 3D through novel view synthesis, how multi-view diffusion ensures geometric consistency, and how feed-forward architectures bypass per-scene optimization entirely. Understand the evolution from slow optimization-based methods to instant 3D generation.
Generative 3D models represent one of the most exciting frontiers in computer vision. While 2D image generation has reached remarkable quality with diffusion models, generating high-quality 3D content remains significantly harder due to the scarcity of 3D training data and the geometric consistency requirements that 2D models do not face.
What is this chapter about? We cover the major paradigms for 3D generation: optimization-based methods that distill knowledge from pretrained 2D diffusion models (DreamFusion, SDS loss), image-conditioned methods that lift 2D observations to 3D, multi-view diffusion models that generate geometrically consistent view sets, 3D-aware GANs that learn structured latent spaces, native 3D diffusion on point clouds and meshes, feed-forward architectures that produce 3D in a single pass, and the applications driving this field forward.
Why does this matter? The ability to generate 3D content from text or images would transform game development, film production, virtual reality, e-commerce (virtual try-on), robotics simulation, and architectural design. Current 3D content creation is expensive and slow, requiring skilled artists working for hours. Generative 3D models promise to democratize 3D content creation, reducing the barrier from professional expertise to a text prompt.
How the topics connect: We start with DreamFusion and SDS loss, the breakthrough that showed 2D diffusion models can supervise 3D optimization. Image-to-3D methods add visual conditioning. Multi-view diffusion addresses consistency issues. 3D-aware GANs offer an alternative generative paradigm. 3D diffusion models operate directly in 3D space. Feed-forward generation eliminates the per-scene optimization loop. We close with applications and future directions that motivate ongoing research.
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Score Distillation Sampling — distilling 2D diffusion knowledge into 3D scene optimization.
From text-only to image-conditioned and multi-view consistent generation
Zero-1-to-3 and LRM for lifting single images into 3D representations.
Joint multi-view generation with geometric conditioning for consistent 3D.
GANs and native 3D diffusion alongside SDS-based methods
EG3D, tri-plane representations, and geometry-aware discriminators.
Denoising diffusion on point clouds, meshes, and 3D latent spaces.
Practical deployment and future directions
LRM and Instant3D — single-pass 3D generation without per-scene optimization.
3D content creation, virtual try-on, and open research problems.
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