houyuanchen111

houyuanchen111 / UniVidX

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[SIGGRAPH 2026 / TOG] Official code of the paper "UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors".

41
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100% credibility
Found May 04, 2026 at 41 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

UniVidX is an advanced tool for creating and editing videos from text descriptions or input videos, handling tasks like generating full scenes, separating foregrounds, or decomposing into material properties.

How It Works

1
🔍 Discover UniVidX

You stumble upon UniVidX while browsing cool video edits online and get excited to create your own magical videos from words or tweak existing clips.

2
📥 Get the tool

Download the ready-to-use kit that includes everything needed to start making videos right away.

3
🎨 Choose your creation

Pick if you want a brand new video from a description or edit an uploaded clip by changing backgrounds, foregrounds, or materials.

4
✨ Describe your vision

Type a simple description like 'a hedgehog chef in a tiny kitchen' or upload your video, and watch the magic begin as it understands exactly what you want.

5
🚀 Generate the video

Hit go, and in moments, your described scene comes alive as smooth, realistic video with perfect details.

🎉 Share your masterpiece

Enjoy your stunning new video, ready to share with friends, feeling like a pro video wizard without any hassle.

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Star Growth

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AI-Generated Review

What is UniVidX?

UniVidX is a Python-based diffusion framework for generating and editing videos across multiple modalities like RGB, albedo, irradiance, normals, alpha matting, foreground, and background. It unifies 15 tasks—text-to-video (t2RAIN/t2RPFB), cross-modal editing (R2PFB/P2RFB), and chained apps like prompt-driven inpainting—using Wan2.1-T2V-14B as backbone. Users configure YAML files for inference scripts that output modality-specific MP4s, with training via Accelerate.

Why is it gaining traction?

This official SIGGRAPH 2026 papers code drop stands out for data efficiency (<1k training videos) and versatility via diffusion priors, slashing the need for task-specific models. Devs dig the YAML-driven CLI for quick text/video inputs to outputs like video editing or matting, plus HF/ModelScope model downloads. As siggraph 2026 timeline heats up (check call for papers, technical papers deadline), it's a timely multimodal generation benchmark.

Who should use this?

Computer vision researchers chasing siggraph 2026 rebuttal or technical papers template should prototype intrinsic video decomposition. Video effects devs handling alpha matting/inpainting (R2PFB→PB2RF) or AR/VR pros needing albedo/normals from text will save weeks. Graphics teams at siggraph asia 2025 github events exploring 2026 priors-based code.

Verdict

Grab it for inference if you're in diffusion video gen—docs shine with examples, 15 modes ready out-of-box. At 41 stars and 1.0% credibility, it's raw post-SIGGRAPH 2026 release; train your own once datasets land.

(198 words)

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