Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping
Authors: Junyoung Seo, Kazumi Fukuda, Takashi Shibuya, Takuya Narihira, Naoki Murata, Shoukang Hu, Chieh-Hsin Lai, Seungryong Kim, Yuki Mitsufuji
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative and quantitative evaluations demonstrate that our model outperforms existing methods in both in-domain and out-of-domain scenarios. |
| Researcher Affiliation | Collaboration | Junyoung Seo1,3 Kazumi Fukuda1 Takashi Shibuya1 Takuya Narihira1 Naoki Murata1 Shoukang Hu1 Chieh-Hsin Lai1 Seungryong Kim3 Yuki Mitsufuji1,2 1Sony AI 2Sony Group Corporation 3KAIST AI |
| Pseudocode | No | The paper describes the method in text and with diagrams (e.g., Figure 3), but no formal pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Project page is available at https://Gen Warp-NVS. github.io. |
| Open Datasets | Yes | We fine-tune the model on multi-view datasets including indoor scene and outdoor scene, i.e., Real Estate10K [52], Scan Net [8], ACID [25]. |
| Dataset Splits | No | The paper mentions using Real Estate10K [52], Scan Net [8], and ACID [25] for training, and tests on Real Estate10K and Scan Net. However, it does not explicitly provide specific details for a separate validation split, such as percentages or sample counts. |
| Hardware Specification | Yes | We initialize our two networks, semantic preserver and diffusion U-net, with Stable Diffusion v1.5 [35], and fine-tune the networks on 2 H100 80GB with a batch size of 48 for 2-3 days, at resolutions of 512 384 and 512 512. |
| Software Dependencies | Yes | We leverage the pretrained Stable Diffusion 1.5 model [35] for both diffusion U-net and semantic preserver network... Stable Diffusion v1.5 Model card: https://huggingface.co/runwayml/stable-diffusion-v1-5 |
| Experiment Setup | Yes | We initialize our two networks, semantic preserver and diffusion U-net, with Stable Diffusion v1.5 [35], and fine-tune the networks on 2 H100 80GB with a batch size of 48 for 2-3 days, at resolutions of 512 384 and 512 512. |