Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[T-PAMI 2025] V3D: Video Diffusion Models are Effective 3D Generators
| Date | Stars |
|---|---|
| 2026-07-31 | 521 |
| 2026-08-06 | 521 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# V3D: Video Diffusion Models are Effective 3D Generators Zilong Chen<sup>1,2</sup>, Yikai Wang<sup>1</sup>, Feng Wang<sup>1</sup>, Zhengyi Wang<sup>1,2</sup>, Huaping Liu<sup>1</sup> <sup>1</sup>Tsinghua University, <sup>2</sup>ShengShu This repository contains the official implementation of [V3D: Video Diffusion Models are Effective 3D Generators](https://arxiv.org/abs/2403.06738). ### What's New [2024.3.14] Our demo is currently available at (here)[https://huggingface.co/spaces/heheyas/V3D]. I will add more checkpoints and more examples recently. ### [Work in Progress] We are currently working on making this completely publicly available (including refactoring code, uploading weights, etc.), so please be patient. ### [arXiv](https://arxiv.org/abs/2403.06738) | [Paper](assets/pdf/V3D.pdf) | [Project Page](https://heheyas.github.io/V3D) | [HF Demo](https://huggingface.co/spaces/heheyas/V3D) ### Video results Single Image to 3D Generated Multi-views https://github.com/heheyas/V3D/assets/44675551/bb724ed1-b9a6-4aa7-9a49-f1a8c8756c2f https://github.com/heheyas/V3D/assets/44675551/4bfaea91-6c5b-40da-8682-30286a916979 Reconstructed 3D Gaussian Splats https://github.com/heheyas/V3D/assets/44675551/894444eb-a454-4bc9-921b-cd0d5764a14d https://github.com/heheyas/V3D/assets/44675551/eda05891-e2c7-4f44-af12-9ccd0bce61d1 https://github.com/heheyas/V3D/assets/44675551/27d61245-b416-4289-ba98-97219ad199a3 https://github.com/heheyas/V3D/assets/44675551/e94d71ff-b8bc-410c-ad2c-3cfb1fbef7fa https://github.com/heheyas/V3D/assets/44675551/a0d1e971-0f8f-4f05-a73e-45271e37a31f https://github.com/heheyas/V3D/assets/44675551/0dac3189-fc59-4e9b-8151-10ebe2711d71 Sparse view scene generation (On CO3D `hydrant` category) https://github.com/heheyas/V3D/assets/44675551/33c87468-b6c0-4fa2-a9bf-6f396b3fa089 https://github.com/heheyas/V3D/assets/44675551/3c03d015-2e56-44de-8210-e33e7ec810bb https://github.com/heheyas/V3D/assets/44675551/1e73958b-04b2-4faa-bbc3-675399f21956 https://github.com/heheyas/V3D/assets/44675551/f70cc259-7d50-4bf9-9c1b-0d4143ae8958 https://github.com/heheyas/V3D/assets/44675551/f6407b02-5ee7-4f8f-8559-4a893e6fd912 ### Instructions: 1. Install the requirements: ``` pip install -r requirements.txt ``` 2. Download our weights for V3D ``` wget https://huggingface.co/heheyas/V3D/resolve/main/V3D.ckpt -O ckpts/V3D_512.ckpt wget https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors -O ckpts/svd_xt.safetensors ``` 3. Run the V3D Video diffusion to generate dense multi-views ``` PYTHONPATH="." python scripts/pub/V3D_512.py --input_path <image file or dir> --save --border_ratio 0.3 --min_guidance_scale 4.5 --max_guidance_scale 4.5 --output-folder <output-dest> ``` 4. Reconstruct 3D assets from generated multi-views Using 3D Gaussian Splatting ``` PYTHONPATH="." python recon/train_from_vid.py -w --sh_degree 0 --iterations 4000 --lambda_dssim 1.0 --lambda_lpips 2.0 --save_iterations 4000 --num_pts 100_000 --video <your generated video> ``` Or using (NeuS) instant-nsr-pl: ``` cd mesh_recon PYTHONPATH="." python launch.py --config configs/videonvs.yaml --gpu <gpu> --train system.loss.lambda_normal=0.1 dataset.scene=<scene_name> dataset.root_dir=<output_dir> dataset.img_wh='[512, 512]' ``` Refine texture ``` python refine.py --mesh <your obj mesh file> --scene <your video> --num-opt 16 --lpips 1.0 --iters 500 ``` ## Acknowledgement This code base is built upon the following awesome open-source projects: - [Stable Video Diffusion](https://github.com/Stability-AI/generative-models) - [3D Gaussian Splatting](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) - [kiuikit](https://github.com/ashawkey/kiuikit) - [Instant-nsr-pl](https://github.com/bennyguo/instant-nsr-pl) Thank the authors for their remarkable job !
Excerpt of 3,867 characters
Read on GitHub
22
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a0989b27bf744032, llm:Repository title and description: "V3D: Video Diffusion Models are Effective 3D Generators" (T-PAMI 2025). Language: Python. No topics provided.
matched fp:a0989b27bf744032, llm:Repository title and description: "V3D: Video Diffusion Models are Effective 3D Generators" (T-PAMI 2025). Language: Python. No topics provided.
matched fp:a0989b27bf744032, llm:Repository title and description: "V3D: Video Diffusion Models are Effective 3D Generators" (T-PAMI 2025). Language: Python. No topics provided.