Notice
Recent Posts
Recent Comments
Link
일 | 월 | 화 | 수 | 목 | 금 | 토 |
---|---|---|---|---|---|---|
1 | 2 | 3 | ||||
4 | 5 | 6 | 7 | 8 | 9 | 10 |
11 | 12 | 13 | 14 | 15 | 16 | 17 |
18 | 19 | 20 | 21 | 22 | 23 | 24 |
25 | 26 | 27 | 28 | 29 | 30 | 31 |
Tags
- 베이지안 정리
- Face Alignment
- CIL
- VQ-diffusion
- Mask diffusion
- ENERGY-BASED MODELS FOR CONTINUAL LEARNING
- DualPrompt
- learning to prompt for continual learning
- PnP algorithm
- Img2pose
- timm
- state_dict()
- VQ-VAE
- CVPR2022
- Mask-and-replace diffusion strategy
- img2pose: Face Alignment and Detection via 6DoF
- Continual Learning
- Class Incremental
- prompt learning
- learning to prompt
- mmcv
- Markov transition matrix
- Vector Quantized Diffusion Model for Text-to-Image Synthesis
- Energy-based model
- Face Pose Estimation
- Facial Landmark Localization
- Class Incremental Learning
- L2P
- requires_grad
- Discrete diffusion
Archives
- Today
- Total
Computer Vision , AI
[One-page summary] NerfDiff: Single image View Synthesis with NeRF guided Distillation from 3D aware Diffusion by Gu et al. 본문
Paper_review[short]
[One-page summary] NerfDiff: Single image View Synthesis with NeRF guided Distillation from 3D aware Diffusion by Gu et al.
Elune001 2024. 1. 16. 00:10● Summary: Use diffusion model to generate multiple view images for NeRF
● Approach highlight
- Training phase: Train a diffusion model to generate images corresponding to multiple views of an object in the training phase.
- Finetuning phase: The diffusion model learned in the training phase is used to train the NeRF by creating multiple views of the image
● Main Results
● Discussion
- In my opinion, if the objects learned in the training phase and the objects in the finetuning phase are different, sample generation will not work well.
- Require multiple views of the object at training time
- Heavy model(NeRF + Diffusion)