Phase 2: Diffusion Models · ~45 minutes · Python
3D Generation
3D is the modality where 2D-to-3D leverage is strongest. The 2023 breakthrough was 3D Gaussian Splatting. The 2024-2026 generative push layers multi-view diffusion + 3D reconstruction on top to produce objects and scenes from a single prompt or photo.
Hiring signal: Understanding of 3d generation internals
What you will learn
- Implement 3d generation from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
Type: Learn Languages: Python Prerequisites: Phase 4 (Vision), Phase 8 · 07 (Latent Diffusion) Time: ~45 minutes
The Problem
3D content is painful:
- Representation. Meshes, point clouds, voxel grids, signed distance fields (SDFs), neural radiance fields (NeRFs), 3D Gaussians. Each has trade-offs.
- Data scarcity. ImageNet has 14M images. The largest clean 3D dataset (Objaverse-XL, 2023) has ~10M objects, most low quality.
- Memory. A 512³ voxel grid is 128M voxels; a useful scene NeRF needs 1M samples/ray. Generation is harder than reconstruction.
- Supervision. For a 2D image you have the pixels. For 3D you usually have a handful of 2D views and have to lift to 3D.
The 2026 stack separates the two problems. First, generate 2D multi-view images with a diffusion model. Second, fit a 3D representation (usually Gaussian splatting) to those images.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Pitfalls, Use It, Ship It, Exercises, Key Terms, Production note: 3D has no shared substrate yet, Further Reading — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
Browse all courses · View pricing · DeVenture Academy