Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Novel View Synthesis with Diffusion Models
Authors: Daniel Watson, William Chan, Ricardo Martin Brualla, Jonathan Ho, Andrea Tagliasacchi, Mohammad Norouzi
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 23,966 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,842 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark 3Di Ms on the SRN Shape Net dataset (Sitzmann et al., 2019) to allow comparisons with prior work on novel view synthesis from a single image. |
| Researcher Affiliation | Industry | Daniel Watson William Chan Ricardo Martin-Brualla Google Research, Brain Google Research, Brain Google Research Jonathan Ho Andrea Tagliasacchi Mohammad Norouzi Google Research, Brain Google Research, Brain Google Research, Brain |
| Pseudocode | Yes | In order to maximize the reproducibility of our results, we provide code in JAX (Bradbury et al., 2018) for our proposed X-UNet neural architecture from Section 2.3. |
| Open Source Code | Yes | In order to maximize the reproducibility of our results, we provide code in JAX (Bradbury et al., 2018) for our proposed X-UNet neural architecture from Section 2.3. |
| Open Datasets | Yes | We benchmark 3Di Ms on the SRN Shape Net dataset (Sitzmann et al., 2019) to allow comparisons with prior work on novel view synthesis from a single image. |
| Dataset Splits | No | No explicit training/validation/test dataset splits with percentages or sample counts are provided for the main model training. |
| Hardware Specification | Yes | we could not ๏ฌt ch=512 in TPUv4 memory without model parallelism |
| Software Dependencies | No | In order to maximize the reproducibility of our results, we provide code in JAX (Bradbury et al., 2018) for our proposed X-UNet neural architecture from Section 2.3. |
| Experiment Setup | Yes | For our neural architecture, our main experiments use ch=256 ( 471M params), and we also experiment with ch=448 ( 1.3B params) in Section 4. One of our early ๏ฌndings that we kept throughout all experiments in the paper is that ch_mult=(1, 2, 2, 4)... We use a learning rate with peak value 0.0001... We use a global batch size of 128. |