Lecture 21 – Generative models for 3D

Notes

Box

Recording

Readings

Deep Generative Models on 3D Representations: A Survey, Shi et al., 2022
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling, Wu et al., NeurIPS 2016
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation, Sanghi et al., CVPR 2022
Learning Representations and Generative Models for 3D Point Clouds, Achlioptas et al., ICML 2018
3D Shape Generation and Completion through Point-Voxel Diffusion, Zhou et al., ICCV 2021
Point-E: A System for Generating 3D Point Clouds from Complex Prompts, Nichol et al, 2022
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images, Gao et al., NeurIPS 2022
Zero-Shot Text-Guided Object Generation with Dream Fields, Jain et al., CVPR 2022
DreamFusion: Text-to-3D using 2D Diffusion, Poole et al., ICLR 2023
Magic3D: High-Resolution Text-to-3D Content Creation, Lin et al., CVPR 2023
Shap-E: Generating Conditional 3D Implicit Functions, Jun and Nichol, 2023
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion, Kamata et al., 2023
ARF: Artistic Radiance Fields, Zhang et al., ECCV 2022
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions, Haque et al., ICCV 2023