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yangzf-1023 / 4C4D

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[CVPR 2026] 4C4D: 4 Camera 4D Gaussian Splatting

junshengzhou.github.io4C4D
54
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100% credibility
Found Apr 15, 2026 at 48 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

4C4D is an open-source framework for reconstructing high-fidelity 4D dynamic scenes from sparse multi-view videos using only four cameras and 4D Gaussian Splatting.

How It Works

1
🔍 Discover 4C4D

You hear about a fun tool that turns videos from just four everyday cameras into lifelike 3D moving scenes, perfect for capturing dynamic moments like cooking or flames.

2
📥 Download and set up

Grab the program and create a simple workspace on your computer so everything is ready to go.

3
📹 Gather your videos

Collect videos from four cameras of the same action, like a spinning drink or sizzling steak, and pull out the picture frames.

4
📐 Add camera details

Tell the program where each camera was pointing by adding simple position notes, using easy helpers if needed.

5
🎓 Teach it your scene

Hit start and watch the magic as the program learns the full 3D movement from your sparse views, building a stunning 4D model.

6
🎬 View your creation

Generate smooth videos from new angles, flying around your reconstructed scene like a pro filmmaker.

✨ Enjoy lifelike 4D magic

Share your high-quality dynamic 3D videos that look real, impressing friends with scenes reborn from just four cameras.

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AI-Generated Review

What is 4C4D?

4C4D is a Python implementation of 4 Camera 4D Gaussian Splatting that reconstructs high-fidelity dynamic 4D scenes from videos shot with just four portable cameras. It enables temporally consistent novel-view rendering without dense camera arrays, using sparse inputs like those from the Neural 3D Video dataset. Users train models via simple CLI commands like `train.py` with YAML configs, then render trajectories or evaluate held-out views with `render.py`.

Why is it gaining traction?

It stands out by handling extreme sparsity—four views suffice—via a neural decaying function that prioritizes geometry over appearance, beating priors on PSNR across datasets with low overlap. As a CVPR 2026 accepted paper on GitHub, it builds on 4DGS and MASt3R for quick setup with preprocessed data and COLMAP/MASt3R pipelines. Devs dig the joint optimization for fast, high-quality 4D outputs.

Who should use this?

CV researchers prototyping sparse-view dynamic reconstruction, graphics devs rendering 4D scenes for AR/VR from phone cams, or teams evaluating CVPR 2026 papers GitHub repos like this for novel-view synthesis. Perfect for N3V-style food prep videos or custom multi-cam setups.

Verdict

Worth forking for sparse 4DGS experiments—solid README, preprocessed data, and conda env make it accessible despite 47 stars and 1.0% credibility score. Early maturity means monitor CVPR 2026 reviews for polish, but it's a strong start for 4C4D4f color workflows.

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